@article {69960, title = {Myocardium and endocardium of the early mammalian heart tube arise from independent multipotent lineages specified at the primitive streak}, journal = {Dev Cell}, year = {2025}, month = {2025 May 20}, abstract = {

The formation of the primitive heart tube from cardiomyocytes and endocardial cells is a key event in mammalian development. Previous studies suggested that cardiomyocytes and endocardial cells segregate from a shared cardiac progenitor around the onset of gastrulation, yet their lineage relationship with other mesodermal tissues remains unclear. Using retrospective and prospective clonal analyses in mouse embryos, we traced cardiomyocyte and endocardial progenitors from the primitive streak to the heart tube. Our results identify two independent mesodermal populations specified around gastrulation onset. While each of these populations is unipotent in producing cardiomyocytes or endocardium, they retain multipotency and contribute to different subsets of non-cardiac mesoderm. Nonetheless, live imaging identifies simultaneous ingression and intermingling of these two mesodermal lineages in the primitive streak, showing their coordinated specification and migration. The proposed model for cardiac progenitor specification will help understanding the origins of congenital heart diseases and designing tissue engineering strategies.

}, issn = {1878-1551}, doi = {10.1016/j.devcel.2025.05.002}, url = {https://doi.org/10.1016/j.devcel.2025.05.002}, author = {Sendra, Miquel and McDole, Katie and de Dios Hourcade, Juan and Temi{\~n}o, Susana and Raiola, Morena and Guignard, Leo and Keller, Philipp J and Dom{\'\i}nguez, Jorge N and Torres, Miguel} } @article {68587, title = {Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations.}, journal = {Nature Biotechnology}, volume = {41}, year = {2023}, month = {2023 Jan 01}, pages = {44-49}, abstract = {

We present a method to automatically identify and track nuclei in time-lapse microscopy recordings of entire developing embryos. The method combines deep learning and global optimization. On a mouse dataset, it reconstructs 75.8\% of cell lineages spanning 1 h, as compared to 31.8\% for the competing method. Our approach improves understanding of where and when cell fate decisions are made in developing embryos, tissues, and organs.

}, keywords = {Animals, Blastocyst, Cell Lineage, Embryo, Mammalian, Mice, Microscopy}, issn = {1546-1696}, doi = {10.1038/s41587-022-01427-7}, author = {Malin-Mayor, Caroline and Hirsch, Peter and Guignard, Leo and McDole, Katie and Wan, Yinan and Lemon, William C and Kainmueller, Dagmar and Keller, Philipp J and Preibisch, Stephan and Funke, Jan} } @conference {69206, title = {Unsupervised Learning of Object-Centric Embeddings for Cell Instance Segmentation in Microscopy Images}, booktitle = {2023 IEEE/CVF International Conference on Computer Vision (ICCV)}, year = {2023}, month = {2023 Oct 01}, publisher = {IEEE}, organization = {IEEE}, address = {Paris, France}, abstract = {

Segmentation of objects in microscopy images is required for many biomedical applications. We introduce object-centric embeddings (OCEs), which embed image patches such that the spatial offsets between patches cropped from the same object are preserved. Those learnt embeddings can be used to delineate individual objects and thus obtain instance segmentations. Here, we show theoretically that, under assumptions commonly found in microscopy images, OCEs can be learnt through a self-supervised task that predicts the spatial offset between image patches. Together, this forms an unsupervised cell instance segmentation method which we evaluate on nine diverse large-scale microscopy datasets. Segmentations obtained with our method lead to substantially improved results, compared to state-of-the-art baselines on six out of nine datasets, and perform on par on the remaining three datasets. If ground-truth annotations are available, our method serves as an excellent starting point for supervised training, reducing the required amount of ground-truth needed by one order of magnitude, thus substantially increasing the practical applicability of our method. Source code is available at github.com/funkelab/cellulus.

}, doi = {10.1109/ICCV51070.2023.01944}, url = {https://ieeexplore.ieee.org/document/10377649/}, author = {Wolf, Steffen and Lalit, Manan and McDole, Katie and Funke, Jan} } @article {68313, title = {Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations.}, journal = {Nature Biotechnology}, year = {2022}, month = {2022 Sep 05}, abstract = {

We present a method to automatically identify and track nuclei in time-lapse microscopy recordings of entire developing embryos. The method combines deep learning and global optimization. On a mouse dataset, it reconstructs 75.8\% of cell lineages spanning 1 h, as compared to 31.8\% for the competing method. Our approach improves understanding of where and when cell fate decisions are made in developing embryos, tissues, and organs.

}, issn = {1546-1696}, doi = {10.1038/s41587-022-01427-7}, author = {Malin-Mayor, Caroline and Hirsch, Peter and Guignard, Leo and McDole, Katie and Wan, Yinan and Lemon, William C and Kainmueller, Dagmar and Keller, Philipp J and Preibisch, Stephan and Funke, Jan} } @article {65971, title = {Characterization of a common progenitor pool of the epicardium and myocardium.}, journal = {Science}, year = {2021}, month = {2021 Jan 07}, abstract = {

The mammalian heart is derived from multiple cell lineages; however, our understanding of when and how the diverse cardiac cell types arise is limited. We mapped the origin of the embryonic mouse heart at single-cell resolution using a combination of transcriptomic, imaging, and genetic lineage labeling approaches. This provided a transcriptional and anatomic definition of cardiac progenitor types. Furthermore, it revealed a cardiac progenitor pool that is anatomically and transcriptionally distinct from currently known cardiac progenitors. Besides contributing to cardiomyocytes, these cells also represent the earliest progenitor of the epicardium, a source of trophic factors and cells during cardiac development and injury. This study provides detailed insights into the formation of early cardiac cell types, with particular relevance to the development of cell-based cardiac regenerative therapies.

}, issn = {1095-9203}, doi = {10.1126/science.abb2986}, author = {Tyser, Richard C V and Ibarra-Soria, Ximena and McDole, Katie and A Jayaram, Satish and Godwin, Jonathan and van den Brand, Teun A H and Miranda, Antonio M A and Scialdone, Antonio and Keller, Philipp J and Marioni, John C and Srinivas, Shankar} } @article {67792, title = {Light sheet fluorescence microscopy.}, journal = {Nature Reviews Methods Primers}, volume = {1}, year = {2021}, month = {2021 Nov 03}, abstract = {

Light sheet fluorescence microscopy (LSFM) uses a thin sheet of light to excite only fluorophores within the focal volume. Light sheet microscopes (LSMs) have a true optical sectioning capability and, hence, provide axial resolution, restrict photobleaching and phototoxicity to a fraction of the sample and use cameras to record tens to thousands of images per second. LSMs are used for in-depth analyses of large, optically cleared samples and long-term three-dimensional (3D) observations of live biological specimens at high spatio-temporal resolution. The independently operated illumination and detection trains and the canonical implementations, selective/single plane illumination microscope (SPIM) and digital scanned laser microscope (DSLM), are the basis for many LSM designs. In this Primer, we discuss various applications of LSFM for imaging multicellular specimens, developing vertebrate and invertebrate embryos, brain and heart function, 3D cell culture models, single cells, tissue sections, plants, organismic interaction and entire cleared brains. Further, we describe the combination of LSFM with other imaging approaches to allow for super-resolution or increased penetration depth and the use of sophisticated spatio-temporal manipulations to allow for observations along multiple directions. Finally, we anticipate developments of the field in the near future.

}, doi = {10.1038/s43586-021-00069-4}, url = {https://www.nature.com/articles/s43586-021-00069-4}, author = {Stelzer, Ernst H. K. and Strobl, Frederic and Chang, Bo-Jui and Preusser, Friedrich and Preibisch, Stephan and McDole, Katie and Fiolka, Reto} } @article {65624, title = {Live-cell imaging in the era of too many microscopes.}, journal = {Current Opinion in Cell Biology}, volume = {66}, year = {2020}, month = {2020 May 26}, pages = {34-42}, abstract = {

At the time of this writing, searching Google Scholar for {\textquoteright}light-sheet microscopy{\textquoteright} returns almost 8500 results; over three-quarters of which were published in the last 5 years alone. Searching for other advanced imaging methods in the last 5 years yields similar results: {\textquoteright}super-resolution microscopy{\textquoteright} (\>16 000), {\textquoteright}single-molecule imaging{\textquoteright} (almost 10 000), SPIM (Single Plane Illumination Microscopy, 5000), and {\textquoteright}lattice light-sheet{\textquoteright} (1300). The explosion of new imaging methods has also produced a dizzying menagerie of acronyms, with over 100 different species of {\textquoteright}light-sheet{\textquoteright} alone, from SPIM to UM (Ultra microscopy) to SiMView (Simultaneous MultiView) to iSPIM (inclined SPIM, not to be confused with iSPIM, inverted SPIM). How then is the average biologist, without an advanced degree in physics, optics, or computer science supposed to make heads or tails of which method is best suited for their needs? Let us also not forget the plight of the optical physicist, who at best might need help with obtaining healthy samples and keeping them that way, or at worst may not realize the impact their newest technique could have for biologists. This review will not attempt to solve all\ these problems, but instead highlight some of the most recent, successful mergers between biology and advanced imaging technologies, as well as hopefully provide some guidance for anyone interested in journeying into the world of live-cell imaging.

}, issn = {1879-0410}, doi = {10.1016/j.ceb.2020.04.008}, author = {Lemon, William C and McDole, Katie} } @article {65057, title = {Light-sheet microscopy and its potential for understanding developmental processes.}, journal = {Annual Review of Cell and Developmental Biology}, volume = {35}, year = {2019}, month = {2019 Oct 6}, pages = {655-81}, abstract = {

The ability to visualize and quantitatively measure dynamic biological processes in vivo and at high spatiotemporal resolution is of fundamental importance to experimental investigations in developmental biology. Light-sheet microscopy is particularly well suited to providing such data, since it offers exceptionally high imaging speed and good spatial resolution while minimizing light-induced damage to the specimen. We review core principles and recent advances in light-sheet microscopy, with a focus on concepts and implementations relevant for applications in developmental biology. We discuss how light-sheet microcopy has helped advance our understanding of developmental processes from single-molecule to whole-organism studies, assess the potential for synergies with other state-of-the-art technologies, and introduce methods for computational image and data analysis. Finally, we explore the future trajectory of light-sheet microscopy, discuss key efforts to disseminate new light-sheet technology, and identify exciting opportunities for further advances.

}, issn = {1530-8995}, doi = {10.1146/annurev-cellbio-100818-125311}, author = {Wan, Yinan and McDole, Katie and Keller, Philipp J} } @article {49466, title = {In toto imaging and reconstruction of post-implantation mouse development at the single-cell level.}, journal = {Cell}, volume = {175}, year = {2018}, month = {2018 Oct 10}, pages = {859-876}, abstract = {

The mouse embryo has long been central to the study of mammalian development; however, elucidating the cell behaviors governing gastrulation and the formation of tissues and organs remains a fundamental challenge. A major obstacle is the lack of live imaging and image analysis technologies capable of systematically following cellular dynamics across the developing embryo. We developed a light-sheet microscope that adapts itself to the dramatic changes in size, shape, and optical properties of the post-implantation\ mouse embryo and captures its development from gastrulation to early organogenesis at\ the cellular level. We furthermore developed a computational framework for reconstructing long-term cell tracks, cell divisions, dynamic fate maps, and maps of tissue morphogenesis across the entire embryo. By jointly analyzing cellular dynamics in multiple embryos registered in space and time, we built a dynamic atlas of post-implantation mouse development that, together with our microscopy and computational methods, is provided as a resource.

}, issn = {1097-4172}, doi = {10.1016/j.cell.2018.09.031}, author = {McDole, Katie and Guignard, Leo and Amat, Fernando and Berger, Andrew and Malandain, Gr{\'e}goire and Royer, Lo{\"\i}c A and Turaga, Srinivas C and Branson, Kristin and Keller, Philipp J} } @article {47267, title = {Real-time three-dimensional cell segmentation in large-scale microscopy data of developing embryos.}, journal = {Developmental Cell}, volume = {36}, year = {2016}, month = {2016 Jan 25}, pages = {225-40}, abstract = {

We present the Real-time Accurate Cell-shape Extractor (RACE), a high-throughput image analysis framework for automated three-dimensional cell segmentation in large-scale images. RACE is 55{\textendash}330 times faster and 2{\textendash}5 times more accurate than state-of-the-art methods. We demonstrate the generality of RACE by extracting cell-shape information from entire Drosophila, zebrafish, and mouse embryos imaged with confocal and light-sheet microscopes. Using RACE, we automatically reconstructed cellular-resolution tissue anisotropy maps across developing Drosophila embryos and quantified differences in cell-shape dynamics in wild-type and mutant embryos. We furthermore integrated RACE with our framework for automated cell lineaging and performed joint segmentation and cell tracking in entire Drosophila embryos. RACE processed these terabyte-sized datasets on a single computer within 1.4\ days. RACE is easy to use, as it requires adjustment of only three parameters, takes full advantage of state-of-the-art multi-core processors and graphics cards, and is available as open-source software for Windows, Linux, and Mac\ OS.

}, doi = {10.1016/j.devcel.2015.12.028}, author = {Stegmaier, Johannes and Amat, Fernando and Lemon, William C and McDole, Katie and Wan, Yinan and Teodoro, George and Mikut, Ralf and Keller, Philipp J} } @article {47082, title = {Efficient processing and analysis of large-scale light-sheet microscopy data.}, journal = {Nature Protocols}, volume = {10}, year = {2015}, month = {2015 Oct 2}, pages = {1679-96}, abstract = {

Light-sheet microscopy is a powerful method for imaging the development and function of complex biological systems at high spatiotemporal resolution and over long time scales. Such experiments typically generate terabytes of multidimensional image data, and thus they demand efficient computational solutions for data management, processing and analysis. We present protocols and software to tackle these steps, focusing on the imaging-based study of animal development. Our protocols facilitate (i) high-speed lossless data compression and content-based multiview image fusion optimized for multicore CPU architectures, reducing image data size 30{\textendash}500-fold; (ii) automated large-scale cell tracking and segmentation; and (iii) visualization, editing and annotation of multiterabyte image data and cell-lineage reconstructions with tens of millions of data points. These software modules are open source. They provide high data throughput using a single computer workstation and are readily applicable to a wide spectrum of biological model systems.

}, doi = {10.1038/nprot.2015.111}, author = {Amat, Fernando and H{\"o}ckendorf, Burkhard and Wan, Yinan and Lemon, William C and McDole, Katie and Keller, Philipp J} } @article {46984, title = {Whole-central nervous system functional imaging in larval Drosophila.}, journal = {Nature Communications}, volume = {6}, year = {2015}, month = {2015 Aug 11}, pages = {7924}, abstract = {

Understanding how the brain works in tight concert with the rest of the central nervous system (CNS) hinges upon knowledge of coordinated activity patterns across the whole CNS. We present a method for measuring activity in an entire, non-transparent CNS with high spatiotemporal resolution. We combine a light-sheet microscope capable of simultaneous multi-view imaging at volumetric speeds 25-fold faster than the state-of-the-art, a whole-CNS imaging assay for the isolated Drosophila larval CNS and a computational framework for analysing multi-view, whole-CNS calcium imaging data. We image both brain and ventral nerve cord, covering the entire CNS at 2 or 5 Hz with two- or one-photon excitation, respectively. By mapping network activity during fictive behaviours and quantitatively comparing high-resolution whole-CNS activity maps across individuals, we predict functional connections between CNS regions and reveal neurons in the brain that identify type and temporal state of motor programs executed in the ventral nerve cord.

}, doi = {10.1038/ncomms8924}, author = {Lemon, William C and Pulver, Stefan R and H{\"o}ckendorf, Burkhard and McDole, Katie and Branson, Kristin M and Freeman, Jeremy and Keller, Philipp J} } @article {45058, title = {Fast, accurate reconstruction of cell lineages from large-scale fluorescence microscopy data.}, journal = {Nature Methods}, volume = {11}, year = {2014}, month = {2014 Jul 20}, pages = {951-8}, abstract = {

The comprehensive reconstruction of cell lineages in complex multicellular organisms is a central goal of developmental biology. We present an open-source computational framework for the segmentation and tracking of cell nuclei with high accuracy and speed. We demonstrate its (i) generality by reconstructing cell lineages in four-dimensional, terabyte-sized image data sets of fruit fly, zebrafish and mouse embryos acquired with three types of fluorescence microscopes, (ii) scalability by analyzing advanced stages of development with up to 20,000 cells per time point at 26,000 cells min(-1) on a single computer workstation and (iii) ease of use by adjusting only two parameters across all data sets and providing visualization and editing tools for efficient data curation. Our approach achieves on average 97.0\% linkage accuracy across all species and imaging modalities. Using our system, we performed the first cell lineage reconstruction of early Drosophila melanogaster nervous system development, revealing neuroblast dynamics throughout an entire embryo.

}, issn = {1548-7105}, doi = {10.1038/nmeth.3036}, author = {Amat, Fernando and Lemon, William and Mossing, Daniel P and McDole, Katie and Wan, Yinan and Branson, Kristin and Myers, Eugene W and Keller, Philipp J} }