POST-DOC POSITION IN DEVELOPMENTAL CELL BIOLOGY AND PHYSIOLOGY (3 years in Nice, IBV, France).
Position available (starting early 2020) to functionally characterize the role of Hedgehog in the inter-cellular and inter-organ communication in Drosophila.
Hedgehog proteinsare known key signaling mediators that govern tissue patterning and homeostasis during both development and adult life. The laboratory is interested in how Hedgehog proteins traffic in the producing tissue and exert their function in the receiving tissue, both in a paracrine and hormonal manner.
We have shown that the Endosomal Sorting Complex Required forTransport (ESCRT) promotes Hedgehog proteins loading on exo-vesicles to exert their effect at long distances. We also have shown recently that circulating Hedgehog has a protective role and have identified targets of Hedgehog signaling in glial cells involved in this process. This newly identified role for Hedgehog is important to provide protection during the ageing process. The post-doctoral project aims to gain further insight into the trafficking, vesicular secretion and the extracellular spread of Hedgehog proteins, both at the intercellular and inter-organ level, using cell biology and genetic technics. Invivoimaging and single molecule tracking (in collaboration with computational science lab) has also been developped on our tissue models and will be further usedto investigatethe dynamics of Hedgehog release and spreading.
Interested candidates should have strong knowledge of, and experience in fly genetics, cell biology and optic microscopy (confocal/spinning disc). The position is funded for 3 years in duration. Candidates must have a Ph.D. degree, and can be nationals of any country.
Selectedreferences: Ayers et al., Dev.Cell2010 vol18, 605–620; Briscoe and Thérond, NatRevMolCellBiol.Vol. 14, 2013; Matusek et al., Nature2014 Dec 4;516(7529): 99-103; D’Angelo et al., Dev.Cell2015 Feb. 9 ; 32, 290-303.
Candidates should send a Curriculum Vitae and a list of three referees to:
Welcome to our monthly trawl for developmental biology (and related) preprints.
This month features a series of preprints on stem cell mechanics and tools to help you make organoids, some nectins and some nestins, plenty of auxin in our plant section, and some phantom crustaceans and macabre French genomics in our ‘Why Not’ section.
They were hosted on bioRxiv, PeerJ, andarXiv. Let us know if we missed anything, and use these links to get to the section you want:
Plexin-B2 is a key regulator of cell mechanics during multicellular organization
Chrystian Junqueira Alves, Rafael Dariolli, Theodore Hannah, Robert J. Wiener, Nicolas Daviaud, Rut Tejero, G. Luca Gusella, Nadejda M. Tsankova, Rodrigo Alves Dias, José Paulo R. Furtado de Mendonça, Evren U. Azeloglu, Roland H. Friedel, Hongyan Zou
Transgene-mediated skeletal phenotypic variation in zebrafish
Charles B. Kimmel, Alexander L. Wind, Whitney Oliva, Samuel D. Ahlquist, Charline Walker, John Dowd, Bernardo Blanco-Sánchez, Tom A. Titus, Peter Batzel, John H. Postlethwait, James T. Nichols
Integrating healthcare and research genetic data empowers the discovery of 49 novel developmental disorders
Joanna Kaplanis, Kaitlin E Samocha, Laurens Wiel, Zhancheng Zhang, Kevin Arvai, Ruth Eberhardt, Giuseppe Gallone, Stefan H Lelieveld, Hilary Martin, Jeremy McRae, Patrick Short, Rebecca Torene, Elke de Boer, Petr Danecek, Eugene James Gardner, Ni Huang, Jenny Lord, Inigo Martincorena, Rolph Pfundt, Margot Reijnders, Alison Yeung, Helger Yntema, DDD study, Lisenka Vissers, Jane Juusola, Caroline Wright, Han Brunner, Helen V Firth, David R Fitzpatrick, Jeffrey C Barrett, Matthew E Hurles, Christian Gilissen, Kyle Retterer
Single-cell analysis of human retina identifies evolutionarily conserved and species-specific mechanisms controlling development
Yufeng Lu, Fion Shiau, Wenyang Yi, Suying Lu, Qian Wu, Joel D. Pearson, Alyssa Kallman, Suijuan Zhong, Thanh Hoang, Zhentao Zuo, Fangqi Zhao, Mei Zhang, Nicole Tsai, Yan Zhuo, Sheng He, Jun Zhang, Genevieve L. Stein-O’Brien, Thomas D. Sherman, Xin Duan, Elana J. Fertig, Loyal A. Goff, Donald J. Zack, James T. Handa, Tian Xue, Rod Bremner, Seth Blackshaw, Xiaoqun Wang, Brian S. Clark
Predicting cellular position in the Drosophila embryo from Single-Cell Transcriptomics data
Jovan Tanevski, Thin Nguyen, Buu Truong, Nikos Karaiskos, Mehmet Eren Ahsen, Xinyu Zhang, Chang Shu, Ke Xu, Xiaoyu Liang, Ying Hu, Hoang V.V. Pham, Li Xiaomei, Thuc D. Le, Adi L. Tarca, Gaurav Bhatti, Roberto Romero, Nestoras Karathanasis, Phillipe Loher, Yang Chen, Zhengqing Ouyang, Disheng Mao, Yuping Zhang, Maryam Zand, Jianhua Ruan, Christoph Hafemeister, Peng Qiu, Duc Tran, Tin Nguyen, Attila Gabor, Thomas Yu, Enrico Glaab, Roland Krause, Peter Banda, DREAM SCTC Consortium, Gustavo Stolovitzky, Nikolaus Rajewsky, Julio Saez-Rodriguez, Pablo Meyer
Single-Cell RNA-Seq Reveals Endocardial Defect in Hypoplastic Left Heart Syndrome
Yifei Miao, Lei Tian, Marcy Martin, Sharon L. Paige, Francisco X. Galdos, Jibiao Li, Alyssa Guttman, Yuning Wei, Jan-Renier Moonen, Hao Zhang, Ning Ma, Bing Zhang, Paul Grossfeld, Seema Mital, David Chitayat, Joseph C. Wu, Marlene Rabinovitch, Timothy J. Nelson, Shuyi Nie, Sean M. Wu, Mingxia Gu
Parkinson’s disease phenotypes in patient specific brain organoids are improved by HP-β-CD treatment
Kyriaki Barmpa, Isabel Rosety, Lisa M. Smits, Jonathan Arias-Fuenzalida, Jonas Walter, Gemma Gomez-Giro, Anna S Monzel, Xiaobing Qing, Gerald Cruciani, Ibrahim Boussaad, Christian Jaeger, Aleksandar Rakovic, Emanuel Berger, Paul Antony, Christine Klein, Rejko Krüger, Philip Seibler, Javier Jarazo, Jens C. Schwamborn, Silvia Bolognin
Atypical neurogenesis in induced pluripotent stem cell (iPSC) from autistic individuals
Dwaipayan Adhya, Vivek Swarup, Roland Nagy, Lucia Dutan Polit, Carole Shum, Kamila Jozwik, Paulina Nowosiad, Irene Lee, David Skuse, Eva Loth, Deirdre Howley, Frances A Flinter, Grainne McAlonan, Maria Andreina Mendez, Jamie Horder, Declan Murphy, Daniel H. Geschwind, Jack Price, Jason Carroll, Deepak P. Srivastava, Simon Baron-Cohen
Plants with self-sustained luminescence
Tatiana Mitiouchkina, Alexander S. Mishin, Louisa Gonzalez Somermeyer, Nadezhda M. Markina, Tatiana V. Chepurnyh, Elena B. Guglya, Tatiana A. Karataeva, Kseniia A. Palkina, Ekaterina S. Shakhova, Liliia I. Fakhranurova, Sofia V. Chekova, Aleksandra S. Tsarkova, Yaroslav V. Golubev, Vadim V. Negrebetsky, Sergey A. Dolgushin, Pavel V. Shalaev, Olesya A. Melnik, Victoria O. Shipunova, Sergey M. Deyev, Andrey I. Bubyrev, Alexander S. Pushin, Vladimir V. Choob, Sergey V. Dolgov, Fyodor A. Kondrashov, Ilia V. Yampolsky, Karen S. Sarkisyan
A proximity biotinylation map of a human cell
Christopher D. Go, James D.R. Knight, Archita Rajasekharan, Bhavisha Rathod, Geoffrey G. Hesketh, Kento T. Abe, Ji-Young Youn, Payman Samavarchi-Tehrani, Hui Zhang, Lucie Y. Zhu, Evelyn Popiel, Jean-Philippe Lambert, Étienne Coyaud, Sally W.T. Cheung, Dushyandi Rajendran, Cassandra J. Wong, Hana Antonicka, Laurence Pelletier, Brian Raught, Alexander F. Palazzo, Eric A. Shoubridge, Anne-Claude Gingras
CRISPR-Cas12a-assisted PCR tagging of mammalian genes
Julia Fueller, Konrad Herbst, Matthias Meurer, Krisztina Gubicza, Bahtiyar Kurtulmus, Julia D. Knopf, Daniel Kirrmaier, Benjamin C. Buchmuller, Gislene Pereira, Marius K. Lemberg, Michael Knop
3D super-resolution deep-tissue imaging in living mice
Mary Grace M. Velasco, Mengyang Zhang, Jacopo Antonello, Peng Yuan, Edward S. Allgeyer, Dennis May, Ons M’Saad, Phylicia Kidd, Andrew E. S. Barentine, Valentina Greco, Jaime Grutzendler, Martin J. Booth, Joerg Bewersdorf
Insights from a survey-based analysis of the academic job market
Jason D. Fernandes, Sarvenaz Sarabipour, Christopher T. Smith, Natalie M. Niemi, Nafisa M. Jadavji, Ariangela J. Kozik, Alex S. Holehouse, Vikas Pejaver, Orsolya Symmons, Alexandre W. Bisson Filho, Amanda Haage
Community Standards for Open Cell Migration Data
Alejandra N. Gonzalez-Beltran, Paola Masuzzo, Christophe Ampe, Gert-Jan Bakker, Sébastien Besson, Robert H. Eibl, Peter Friedl, Matthias Gunzer, Mark Kittisopikul, Sylvia E. Le Dévédec, Simone Leo, Josh Moore, Yael Paran, Jaime Prilusky, Philippe Rocca-Serra, Philippe Roudot, Marc Schuster, Gwendolien Sergeant, Staffan Strömblad, Jason R. Swedlow, Merijn van Erp, Marleen Van Troys, Assaf Zaritsky, Susanna-Assunta Sansone, Lennart Martens
Wikidata as a FAIR knowledge graph for the life sciences
Andra Waagmeester, Gregory Stupp, Sebastian Burgstaller-Muehlbacher, Benjamin M. Good, Malachi Griffith, Obi Griffith, Kristina Hanspers, Henning Hermjakob, Kevin Hybiske, Sarah M. Keating, Magnus Manske, Michael Mayers, Elvira Mitraka, Alexander R. Pico, Timothy Putman, Anders Riutta, Núria Queralt-Rosinach, Lynn M. Schriml, Denise Slenter, Ginger Tsueng, Roger Tu, Egon Willighagen, Chunlei Wu, Andrew I. Su
Guidelines for reporting single-cell RNA-Seq experiments
Anja Füllgrabe, Nancy George, Matthew Green, Parisa Nejad, Bruce Aronow, Laura Clarke, Silvie Korena Fexova, Clay Fischer, Mallory Ann Freeberg, Laura Huerta, Norman Morrison, Richard H. Scheuermann, Deanne Taylor, Nicole Vasilevsky, Nils Gehlenborg, John Marioni, Sarah Teichmann, Alvis Brazma, Irene Papatheodorou
A team of neuroscientists led by Professor Christiana Ruhrberg (UCL, UK) and Professor Anna Cariboni (University of Milan, Italy) have found two molecules that work together to help set up the sense of smell and pave the way to puberty in mice. These findings, reported in the journal Development, may help our understanding of why patients with the inherited condition Kallmann syndrome cannot smell properly and cannot start puberty without hormone treatment.
Aficionados of 1990s jazz and fans of David Lynch’s Twin Peaks might remember the distinctive contralto vocals of “Little” Jimmy Scott. Jimmy’s naturally high singing voice was caused by a rare genetic disease, known as Kallmann syndrome, which affects about 1 in 30,000 males and 1 in 120,000 females.
Kallmann syndrome is caused by the lack of a hormone that stimulates the brain to produce signals needed to reach sexual maturity. As a result, people with the condition don’t go through puberty and instead retain a child-like stature, no sex drive and underdeveloped genitals. Currently, the most common treatment is hormone-replacement therapy to bypass the brain and kick-start puberty. Unlike similar reproductive conditions, Kallmann syndrome patients also have no sense of smell – a tell-tale sign of this particular disorder.
Now, research has identified two molecules, called PLXNA1 and PLXNA3, that might be linked to the condition. Scientists have found that both molecules are present in nerves that extend from the nose into the brain of developing mice. These nerves transmit signals essential for the sense of smell and also guide hormone-secreting nerve cells from their place of origin in the nose to their destination in the brain, where they regulate the onset of puberty. The study has revealed that both types of nerve are not wired properly when PLXNA1 and PLXNA3 are absent in developing mice. Consequently, the brain regions that process smells are poorly formed and the brain also lacks the puberty-promoting nerve cells – the same symptoms shown by Kallmann syndrome patients.
“By studying the mouse as a model organism, we have identified a pair of genes that can cause an inherited condition with symptoms similar to human Kallmann syndrome. This is an important finding, because the nerves that convey our sense of smell and that guide the puberty-inducing nerve cells arise in a very similar way during the development of mice and humans whilst they are still in the womb,” explained Professor Christiana Ruhrberg, who led the UK team.
This research gives hope to patients with an unknown cause of Kallmann syndrome by testing for defects in the PLXNA3 gene together with PLXNA1, which has been previously implicated. The lead author from the University of Milan, Professor Anna Cariboni added, “Although Kallmann syndrome can be treated with hormone injections if diagnosed early, knowing the underlying genetic causes can make a huge difference to speed up diagnosis and give treatment to the right patients at an earlier time.”
The image contains a circular structure within the developing nose that gives rise to puberty-inducing nerve cells, shown in green. Some of these nerve cells leave their birth place to travel in small clumps along nerve cables. These cells and the cables also have the PLXNA3 molecule, which is coloured red, but appears yellow, because of the red and green overlap. All cells are also highlighted in blue. CREDIT: Roberto Oleari, University of Milan.
Pre-trained Models for Developmental Biology Authors: Bradly Alicea, Richard Gordon, Abraham Kohrmann, Jesse Parent, Vinay Varma
Our virtual discussion group (DevoWormML) has been exploring a number of topics related to the use of pre-trained models in machine learning (specifically deep learning). Pre-trained models such as GPT-2 [1], pix2pix [2], and OpenPose [3] are used for analyzing many specialized types of data (linguistics, image to image translation, and human body features, respectively) and have a number of potential uses for the analysis of biological data in particular. It may be challenging to find large, rich, and specific datasets for training a more general model. This is often the case in the fields of Bioinformatics or Medical Image analysis. Data acquisition in such fields is often restricted due to the following factors:
* privacy restrictions inhibit public access to personal information, and may impose limits on data use.
* a lack of labels and effective metadata for describing cases, variables, and context.
* missing data points, which require a strategy to normalize and can make the input data useless.
We can use these pre-trained models to extract a general description of classes and features without requiring a prohibitive amount of training data. We estimate that the amount of required training data may be reduced by an order of magnitude. To get this advantage, pre-trained models must be suitable to the type of input data. There are a number of models specialized for language processing and general use, but options are fewer within the unique feature space of developmental biology, in particular. In this post, we will propose that developmental biology requires a specialized pre-trained model.
This vision for a developmental biology-specific pre-trained model would be specialized for image data. Whereas molecular data might be better served with existing models specialized for linguistic- and physics-based models, we seek to address several features of developmental biology that might be underfit using current models:
* cell division and differentiation events.
* features demonstrating the relationship between growth and motion.
* mapping between spatial and temporal context.
Successful application of pre-trained models is contingent to our research problem. Most existing pre-trained models operate on two-dimensional data, while data types such as medical images are three-dimensional. A study by Raghu et.al [4] suggests techniques specified by pre-trained models (such as transfer learning by the ImageNet model) applied to a data set of medical images provides little benefit to performance. In this case, performance can be improved using data augmentation techniques. Data Augmentation, such as adding versions of the images that have undergone transformations such as magnification, translation, rotation, or shearing, can be used to add variability of our data and improve the generalizability of a given model.
One aspect of pre-trained models we would like to keep in mind is that models are not perfect representations of the phenomenology we want to study. Models can be useful, but are often not completely accurate. A model of the embryo, for example, might be based on the mean behavior of the phenomenology. Transitional states [5], far-from-equilibrium behaviors [6], and rare events are not well-suited to such a model. By contrast, a generative model that considers many of these features might generally underfit the mean behavior. We will revisit this distinction in the context of “blobs” and “symbols”, but for now, it appears that models are expected to be both imperfect and incomplete.
The inherent imperfection of models is both good and bad news for our pursuit. On the one hand, specialized models cannot be too specific, lest they overfit some aspects of development but not others. Conversely, highly generalized models assume that there are universal features that transcend all types of systems, from physical to social, and from artificial to natural. One example of this is found in complex network models, widely used to represent everything from proteomes to brains to societies. In their general form, complex network models are not customized for specific problems, relying instead on the node and edge formalism to represent interactions between discrete units. But this also requires that the biological system be represented in a specific way to enforce the general rules of the model. For example, a neural network’s focus on connectivity requires representations of a nervous system to be simplified down to nodes and arcs. As opposed to universality, particularism is an approach that favors the particular features of a given system, and does not require an ill-suited representation of the data. Going back to the complex networks example, there are specialized models such as multi-level networks and hybrid models (dynamical systems and complex networks) that solves the problem of universal assumptions.
Another aspect of pre-trained models is in balancing the amount of training data needed to produce an improvement in performance. How much training data can we save by applying a pre-trained model to our data set? We can reformulate this question more specifically to match our specific phenomenon and research interests. To put this in concrete terms, let us consider a hypothetical set of biological images. These images can represent discrete points in developmental time, or a range of biological diversity. Now let us suppose a developmental phenotype for which we want to extract multiple features. What features might be of interest, and are those features immediately obvious?
In the DevoWorm group (where we mostly deal with embryogenetic data), we have approached this in two ways. The first is to model the embryo as a mass of cells, so that the major features of interest are the shape, size, and position of cells in an expanding and shifting whole. Last summer, we worked on applying deep learning to
While these models were effective for discovering discrete structural units (cells, filaments), they were not as effective at directly modeling movement, currents, or transformational processes. The second way we have approached this is to model the process of cell division and differentiation as a spatial and discrete temporal process. This includes the application of representational models such as game theory [7] and cellular automata [8]. This allows us to identify more subtle features that are not directly observable in the phenotype, but are less useful for predicting specific events or defining a distinct feature space.
Our model must be capable of modeling multiple structural features concurrently, but also sensitive to scenarios where single sets of attributes might yield more information. Ideally, we desire a training dataset that perfectly balances “biologically-typical” motion and transformations with clearly masked shapes representing cells and other phenotypic structures. Generally speaking, the greater degree of natural variation in the training dataset, the more robust the pre-trained model will turn out to be. More robust models will generally be easier to use during the testing phase, and result in a reduction in the need for subsequent training.
Finally, specialized pre-trained models bring up the issue of how to balance rival strategies for analyzing complex processes and data features. Conventional artificial intelligence techniques have relied on a representation which relies on the manipulation of symbols or a symbolic layer that results from the transformation of raw data to a mental framework. By contrast, modern machine learning methods rely on data to build a series of relationships that inform a classificatory system. While a combination of these two strategies might seem obvious, it is by no means a simple matter of implementation [9]. The notion of “blobs” (data) versus “symbols” (representations) draws on the current debate related to data-intensive representations versus formal (innate) representations [10-12], which demonstrates the timeliness of our efforts. Balancing these competing strategies in a pre-trained model allows us to more easily bring expert knowledge or complementary data (e.g. gene expression data in an analysis of embryonic phenotypes) to bear.
We will be exploring the details of pre-trained models in future discussions and meetings of the DevoWormML group. Please feel free to join us on Wednesdays at 1pm UTC at https://tiny.cc/DevoWorm or find us on Github (https://github.com/devoworm/DW-ML) if you are interested in discussing this further. You can also view our previous discussions on the DevoWorm YouTube channel, DevoWormML playlist (https://bit.ly/2Ni7Fs2).
[2] Isola, P., Zhu, J-Y., Zhou, T., Efros, A.A. (2017). Image-to-Image Translation with Conditional Adversarial Nets. Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR).
[3] Cao, Z., Hidalgo, G., Simon, T., Wei, S-E., and Sheikh, Y. (2018). OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields. arXiv, 1812.08008.
[4] Raghu, M., Zhang, C., Kleinberg, J.M., and Bengio, S. (2019). Transfusion: Understanding Transfer Learning for Medical Imaging. arXiv, 1902.07208.
[5] Antolovic, V., Lenn, T., Miermont, A., Chubb, J.R. (2019). Transition state dynamics during a stochastic fate choice. Development, 146, dev173740. doi:10.1242/dev.173740.
[6] Goldenfeld, N. and Woese, C. (2011). Life is Physics: Evolution as a Collective Phenomenon Far From Equilibrium. Annual Review of Condensed Matter Physics, 2, 375-399. doi:10.1146/annurev-conmatphys-062910-140509.
[7] Stone, R., Portegys, T., Mikhailovsky, G., and Alicea, B. (2018). Origins of the Embryo: Self-organization through cybernetic regulation. Biosystems, 173, 73-82. doi:10.1016/j.biosystems.2018.08.005.
[8] Portegys, T., Pascualy, G., Gordon, R., McGrew, S., and Alicea, B. (2016). Morphozoic: cellular automata with nested neighborhoods as a metamorphic representation of morphogenesis. In “Multi-Agent Based Simulations Applied to Biological and Environmental Systems“. Chapter 3 in “Multi-Agent-Based Simulations Applied to Biological and Environmental Systems”, IGI Global.
[9] Garnelo, M. and Shanahan, M. (2019). Reconciling deep learning with symbolic artificial intelligence: representing objects and relations. Current Opinion in Behavioral Sciences, 29, 17–23.
[10] Zador, A.M. (2019). A critique of pure learning and what artificial neural networks can learn from animal brains. Nature Communications, 10, 3770.
The Department of Biological Sciences at Auburn University invites applications for a tenure-track faculty position beginning Fall 2020 at the rank of Assistant Professor in Developmental Biology with an emphasis in either Plant or Vertebrate/Terrestrial Systems.
We seek highly collaborative candidates who will examine fundamental mechanisms governing developmental processes. A successful candidate is expected to establish an extramurally funded, internationally recognized research program focused on developmental biology. Instructional responsibilities include development of graduate and/or undergraduate courses in developmental biology related to their area of emphasis. Faculty will join recent hires in Evolutionary-Developmental Biology of Marine Invertebrate Systems and an emerging group of Developmental Biologists in the department.
Applicants must have a Ph.D. in Biological Sciences or a closely related discipline at the time employment begins, and relevant postdoctoral experience. The successful candidate must possess excellent written and interpersonal communication skills. Desired qualifications include a strong record of publication, teaching experience, and demonstrated (or potential) ability to acquire extramural funding.
The MRC Weatherall Institute of Molecular Medicine (WIMM) has fully funded 4-year Prize PhD (DPhil) Studentships available to start in October 2020. These Studentships are open to outstanding students of any nationality who wish to train in experimental and/or computational biology.
The Institute is a world leading molecular and cell biology centre that focuses on research with application to human disease including bioinformatics analysis. It houses over 500 research and support staff in more than 50 research groups working on a range of fields in Haematology, Gene Regulation & Epigenetics, Stem Cell Biology, Computational Biology, Cancer Biology, Human Genetics, Infection & Immunity. The Institute is committed to training the next generation of scientists in these fields through its Prize PhD Studentship Programme.
The fully funded studentships include a stipend of £18,000 per annum and cover University and College fees.
Further information on the studentships, how to apply, and the projects available can be found at:
The dependence of a protein’s function on its structure is a well-known phenomenon. Back in 1970’s, it was suggested that most proteins would fold into one energetically stable or favorable conformational state in the cell determined by their primary amino acid sequence. This led to the notion of “one sequence to one structure to one function”. Later, the identification of prions which have more than one stable structure or conformation in the same cell suggested there are exceptions to this rule. However, in the early days of this discovery, one of these conformations was nonfunctional and disease causing; therefore, the dogma still held true. Later, the discovery of functional non-toxic counterparts of prions, called prion-like proteins, challenged the dogma. These proteins not only existed in different conformations but also had different functions associated with different conformations, expanding the functional space proteins can occupy.
Today, prion-like proteins are shown to have roles in different physiological processes including adaptation to changing environmental conditions, immune response and memory formation. In all of these processes, they act as transcriptional or translational regulators, or signaling components at the molecular level, leading to a global change in cellular response. Most of these prion-like proteins are well-studied yeast prions and are often determinants of heritable phenotypes. When we look at higher order organisms, the examples of such proteins are restricted. Recent computer-based screens show these proteins are prevalent throughout all kingdoms of life; yet it is still unknown what functions they serve in different conformational states in normal physiology of higher order organisms.
Walking on Drosophila Proteome
Kausik Si’s research lab (https://research.stowers.org/silab/) at Stowers Institute for Medical Research in Kansas City is home to the very first prion-like protein found in Drosophila, called Orb2, which is important for the persistence of memory. The discovery of Orb2 and the commonality of prion-like proteins in yeast led to the idea that prion-like proteins could work in many other physiological processes. The Si lab conducted a small-scale computational screen on the Drosophila proteome to find out novel prion-like proteins. Focusing on the top scoring proteins, his team performed an experimental screen using exogenously expressed versions of the selected candidate proteins with various conventional techniques used to characterize prion-like properties (see the publication for technical details). This eventually led to a small set of 5 proteins that could be used to explore the effects of their conformation on their functions in vivo. Some of these proteins have well-known functions, and the only thing needed was to relate the conformations to these functions. The most challenging part was analyzing the proteins which did not have any functional information but only a protein sequence. Herzog, once called CG5830, was one such protein. It was at this point as a naïve but venturesome graduate student, I came into the picture!
Things got a little bit spicier
What one does when faced with a newly discovered completely unknown protein: a homology search to find out what similar proteins do in other organisms. Herzog’s homolog in mammals is a phosphatase, called SCP1, which regulates RNA polymerase II activity. When I took over the Herzog project, I first did several experiments to understand whether Herzog is really a homolog of SCP1. However, I found that even though Herzog has in vitro phosphatase activity, it doesn’t have functional similarity to SCP1 in the cell. So, I was back to where I started.
A protein has different roles at each level of biological organization, from molecules to organism, and the particular functions it performs at one level add up and lead the way to the next level, eventually telling us how that protein contributes to the life of the organism. Now, to understand Herzog completely, I needed to systematically characterize its functions at the molecular, cellular, and organismal levels. I had a lot of questions: Is it a phosphatase in the cell? If so, what are the substrates? Where does it localize? What does it partner with to do its job? What does it control? How important it is for the fly? Finally and most importantly what is the connection between its conformation and function? To answer all these questions, I needed a flexible medium of work. Luckily enough, I was working with Drosophila melanogaster, which has various tools and resources making it easy to manipulate at all these different levels.
Herzog all the way down
Before I get into the details of our story, I want to point you to the illustration below to put things in context. Along with our publication, I decided to submit potential cover images. One of those images was drawn by Stephanie Nowatarski, PhD (a really good old Drosophilist, now Planari-st and artist; http://stephanienowotarski.com/) in collaboration with Mol Mir (a researcher and art-maker; https://molmir.com/). It depicts our model from bottom (molecular structure) to top (animal viability)- highlighting the scalar structure of the analysis we performed in the paper.
This Illustration depicts the paper’s model from bottom to top- starting with the prion structure of Herzog associated with membrane and likely associated with phosphorylated proteins, acting as a phosphatase. The color of Herzog is carried up through the scale of the organism from cell to tissue to embryogenesis and depicts both Herzog’s localization at membranes on a tissue level and its importance in embryogenesis. The top tiers complete the view of developmental biology, from larvae to adult fly.
The two main strategies I followed to discover the function of Herzog were: 1- To look for the phenotypic effect of herzog mutations on the organism; and 2-To follow its expression pattern over time and space.
CRISPR-derived herzog mutant lines, which lack the middle phosphatase domain and the rest of the C-terminal of the protein, were embryonic lethal in the F2 generation. To understand when and where they had defects during embryonic development, I first examined their cuticles. I observed that mutants do not have the characteristic denticle pattern of a differentiated embryo. Referring to the famous “Heidelberg screen”, it was clear that herzog mutant embryos had defects in segment polarity, which is regulated mainly by two genes, i.e., engrailed and wingless. In embryos, engrailed starts expressing just after cellularization at the beginning of gastrulation. Staining of herzog mutant embryos right at this stage for engrailed showed us a diffused expression of engrailed in these mutants compared to wild type. When I looked at wingless, which is normally expressed at the posterior end of each segment later in development, I observed that while herzog mutants had the correct number of segments, there was an additional stripe of wingless in each segment (this data did not make it to the paper). This suggested that the diffused expression pattern of engrailed led additional cells to gain wingless identity, causing segments to lose their polarity. Most importantly, this told us that herzog has a role in establishing the A-P axis of each segment, acting as an antagonist of wingless signaling which begins with gastrulation. But how about the normal expression pattern of herzog? For that, the herzog mutant phenotype suggested two possibilities: 1- Herzog protein is both expressed and functional at this specific time point, like the other patterning genes, or 2-Herzog is there all along, but something happens at this time point such that the protein becomes functional.
Let’s find the answer! For that, I endogenously tagged Herzog and followed it in the fixed embryos. I observed that Herzog is ubiquitously expressed on the membrane throughout embryonic development. In contrast to the patterning genes it regulates, Herzog does not have a specific expression pattern, which ruled out the first possibility I suggested above. Then I started thinking how I go about the second possibility. Wait a minute! I had embryos with fluorescent Herzog which I could follow live and focus closely! Collaborating with an awesome microscopist, @jeff_j_lange, we saw something happening to the protein just at the start of gastrulation: it is diffusely localized along the membrane until the end of cellularization, and when gastrulation begins, it changes to a punctate form on the membrane, and the punctate pattern persists throughout the rest of embryonic development. Now, I had a phenotype correlating a physical state change in the protein, which raised the curious question: Are the conformations of these two different physical states of Herzog different as would be the case for a prion-like protein? Before answering this question, I should mention that prion-like proteins can exist in at least two different conformations in the same cell: a monomer and an aggregate which can attain various physical conformations with different stabilities, ranging from flexible liquid droplets to very stable amyloids. To understand Herzog’s conformations, I followed it by western blot analysis at different stages of embryogenesis, and I saw that it exists as soluble low molecular weight monomers in all stages of embryogenesis. Interestingly, it transforms into high molecular weight aggregates, just after gastrulation, which correlates with the timing of its punctate pattern. Insolubility of these aggregates, even under the extreme denaturing conditions of boiling and detergents, suggests a stable conformation, like amyloid. In line with this idea, I found that Herzog protein staining colocalizes with an amyloid specific dye in embryos and that purified embryonic Herzog protein can be recognized with amyloid specific antibodies. So, I now had a protein that changes to an amyloid like state during gastrulation, and this coincides with its segment polarity phenotype. However, I still needed to know what this means at the molecular/biochemical level.
To understand the molecular function of Herzog, I thought I could build a reporter system to detect Herzog’s enzymatic activity in the presence or absence of aggregation. For this, I needed to answer two important questions: 1- Which part of the protein causes it to aggregate? and 2- What are the substrates of Herzog? Using truncated versions of Herzog, I found that N terminal prion-like domain of Herzog is responsible for both its aggregation and membrane localization. With proteomic analyses, I found that it interacts with well-known developmental regulators, which have roles in TGFβ/BMP, EGF and FGF signaling pathways and cell cycle. Focusing on one potential candidate called Dah, which was previously found to be dephosphorylated during gastrulation by an unknown phosphatase, I designed an enzymatic assay for Herzog and found that Herzog dephosphorylates Dah, for which N terminal prion-like domain is required. Replacing Herzog’s prion-like domain with a known amyloid forming prion-like domain (which also had a membrane targeting motif) rescued the enzymatic activity. This meant that the phosphatase activity of Herzog depends on its amyloid-like aggregation through its N terminal prion-like domain on the membrane. However, this did not tell us whether there is a clear distinction between activities of monomers and aggregates. In collaboration with an experienced structural biologist, Ruben Hervas Milan, we recapitulated the enzymatic activity of Herzog aggregates with purified protein from embryos. Importantly, we found that aggregates were active, and monomers did not have phosphatase activity. Moreover, when we allowed monomeric recombinant Herzog protein to self-assemble into amyloid-like fibrils, we observed a dramatic increase in enzymatic activity, while dissociation of these fibrils with an amyloid inhibitor abolished the activity.
Putting all these pieces together, I can now say that Herzog’s switch into amyloid-like aggregates is a developmentally regulated process resulting in its enzymatic activation that is essential for the patterning of the embryo.
What is ahead of us?
Although it was previously shown that knock down of prion-like homolog of prion protein in PrP knock out mouse results in an embryonic phenotype, how or whether the conformational change of these proteins affects development has not been studied. Herzog exemplifies for the first time how a protein conformational switch into a higher order amyloid state regulates a specific process in embryonic development. As the first natural example of an amyloid enzyme, Herzog demonstrates an alternative mode of enzymatic regulation: the use of a prion-like domain to regulate catalytic activity with conformational change. Looking ahead, we want to understand how the amyloid structure orients the catalytic domain of the enzyme to alter its activity; how the conformational switch of Herzog is regulated during development and how the aggregation dynamics regulate embryonic patterning.
Looking at its interactors, Herzog seems to have several potential functions at the intersection of multiple developmental signaling pathways. These pathways are inherently dynamic but lead to stable information to carry development forward. How can an enzyme with a seemingly stable conformation like amyloid make it in the dynamic environment of signaling pathways and so in development? Recent studies have shown that identical polypeptides can fold into multiple, distinct amyloid conformations and that amyloid structure can dynamically form and disappear via post translational modifications. We speculate that such structural flexibility and heterogeneity would allow a protein like Herzog to adopt stable yet dynamic conformational states. Moreover, these features may also lead to functional diversity such that a an amyloid-like protein can form distinct functional units, with even opposing functions, in the same cell and can maintain this functional diversity with the help of its stability. Development, which needs to accommodate the changing environment, might utilize such molecular stability and flexibility to tune the time course of development. Therefore, our study lends support to the idea that there can be other prion-like conformational functional switches regulating other important developmental events.
In research that holds potential for prenatal health and brain injury, Scripps Research scientists identify cellular workings that stop and restart early brain development. Press release from Scripps Research, La Jolla.
We all know that food is essential to healthy development of the brain and body, especially in the earliest stages of life. But exactly how early brain growth is affected by nutrition is not as well understood, especially on a cellular level.
One reason for this lack of understanding is simply the difficulty of studying animals before they are born. But in a study involving tadpoles, which develop entirely outside of a mother’s womb, scientists at Scripps Research were able to unearth new findings about how brain cells respond to—and recover from—lack of nutrition.
“With tadpoles, we can look at early stages of brain development that are typically inaccessible to us,” says cell biologist Caroline McKeown, PhD, a senior staff scientist in the neuroscience lab of Hollis Cline, PhD, and lead author of the study. “This study showed us, for the first time in a vertebrate species, the cell signaling pathways that are integral to nutrient-responsive cell division in neural stem cells. These findings may lead to new approaches for starting and stopping cell growth in the brain.”
After periods without food, nutrition induces widespread proliferation of neural stem cells (green) in the tadpole brain. Mature neurons are shown in red. (Image courtesy of the Cline lab.)
The research, which appears in the journal Development, has multiple potential applications—including improved prenatal care in humans. McKeown said the findings also will contribute to on-going research in the lab on the role of neural stem cells in recovery from brain injury.
Typically, in a Xenopus tadpole and in most animals, stem cells known as “neural progenitors” flourish during early stages of development. These cells eventually mature into neurons, the cell type in the brain the controls thought and action.
In a previous study, McKeown and Cline found that when the tadpoles were deprived of food, their neural progenitor cells stopped dividing and their body growth decreased, but the animals remained alive and their behavior appeared normal. Surprisingly, if tadpoles were able to access food within about nine days, neural progenitor cells in the brain started dividing again and the tadpoles caught up to the growth state where they would have been if food had always been available.
What captured McKeown’s attention were the life-or-death questions: What triggered the neural progenitor cells to be able to divide again? And how did it work? In the new study, she and Cline identified the cellular mechanisms underlying this developmental response.
“We know a lot of these fundamental cellular events are conserved across animal species, so it’s possible that mammalian species are also capable of this kind of resilience to prenatal nutrient deprivation,” McKeown says.
Once the researchers found that early brain development could bounce back after periods without food, they wanted to understand what was happening on a cellular level to tell neural progenitors to stop dividing and to start back up. They traced it to a well-known signaling pathway known as mTOR (short for “mammalian target of rapamycin”), which is a central regulator of cell metabolism, growth, proliferation and survival.
Interestingly, even without providing the tadpoles with any food, their brains could be relaunched into growth mode by activating the insulin receptor that sits on the surface of neuronal progenitor cells, Cline says. Insulin is a hormone that allows cells to use sugars from food as energy and can activate mTOR signaling. Being able to bypass the need for food on a cellular level could advance medical therapies for poor nutrition.
By carefully tracking the neural progenitor cells over time, McKeown also discovered that they were poised to divide as soon as the nutrient signals reached them. This meant the cells had halted their progression when they were right on the verge of dividing. This is typically seen in cells under stress, and clearly starvation is a type of stress.
“Studying the ability of tadpoles to respond to environmental uncertainties helped increase our understanding of conserved cellular events controlling brain development,” McKeown says.
“The observation that food affects brain cell division was already known, but nobody dug more deeply into how food was having that effect,” adds Cline, Hahn Professor of Neuroscience and chair of the Department of Neuroscience in La Jolla. “We envision this knowledge becoming useful in understanding what can go wrong in the absence of maternal nutrition, and how important it is to respond quickly to a such an event.”
This research was supported by National Institutes of Health (EY011261, EY027473), Dart NeuroScience LLC and an endowment from the Hahn Family Foundation.
Biscutella laevigata – the subject of many of Saunders’ important plant breeding experiments. Photo: Atriplexmedia CC-BY-SA 3.0
The history of genetics has a few famous partnerships – such as James Watson and Francis Crick or Francois Jacob and Jacques Monod. But there’s one pair without whom this podcast wouldn’t exist at all, and that’s Edith Rebecca Saunders and William Bateson, who founded The Genetics Society one hundred years ago.
But while Bateson tends to get the glory, particularly for his popularisation of Gregor Mendel’s ideas about heredity, much less is heard about Saunders – the ‘mother of British plant genetics’, as she was referred to by JBS Haldane.
She was one of the first women to pursue a scientific education and research career at Cambridge University in an era when women were excluded from formal lectures and prevented from graduating. Rather than being a research assistant, Saunders was an equal colleague of Bateson.
She was a formidable teacher and researcher, eventually becoming director of the Balfour Biological Laboratory for Women in Cambridge, and made important contributions to genetics through her meticulous plant-breeding experiments. Saunders was also a key member of many scientific societies, and co-founded The Genetics Society together with Bateson in 1919.
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By Amnon Sharir (UCSF), Allon M Klein (HMS), Ophir D Klein (UCSF)
As most mouse geneticists know, treating a rodent with malocclusion of its front teeth can be a Sisyphean task: as much as one trims the tooth, it grows right back within a few days (Figure 1). The basis of this often-frustrating situation is that rodent incisors contain a group of adult stem cells (SCs) at the base of the tooth that drive continuous growth of the organ. These SCs produce a constant supply of new cells that replace the cells that are lost from the tip of the tooth due to normal wear or tooth breakage. In contrast, human teeth are quite different: once they are mature, they no longer grow. As a result, wear and tear of the enamel, the hard layer that covers the teeth, as well as diseases like caries that disrupt the enamel, cannot be self-repaired.
Figure 1: Top: In vivo µCT immediately (day 0) after trimming ~1 mm from the tip of one incisor, and 4 days later, demonstrating the remarkable regenerative capacity of the of mouse incisors. In contrast, a human broken tooth cannot self-repair. (Picture is a courtesy of Robert Ho (UCSF)). Bottom: Illustration showing the location of the incisor stem cell niche within the jaw bone. Magnification of the boxed area showing the epithelium (in green), the tissue which produces the ameloblasts that lay down enamel.
Because of their amazing regenerative abilities, our lab (klein.ucsf.edu), along with a number of others, has become very interested in mouse incisor SCs. We believe that understanding the mechanisms by which animals like mice normally renew their teeth will enable us to lay a foundation for human tooth regeneration. The mouse incisor provides a powerful platform for uncovering cellular behaviors, signaling pathways and transcriptional interactions that govern self-renewal and differentiation, and these findings have important implications for the field of SC biology. Most of the discoveries in the incisor epithelium, the tissue which produces the ameloblasts that lay down enamel (Figure 1), have resulted from candidate approaches. These studies identified several genes expressed by cells in the most proximal region of the incisor epithelium that give rise to the differentiated ameloblasts over a long period of time. Of note, these markers also largely, but not exclusively, co-localize with cells that are slowly cycling and therefore retain a label for long periods (label-retaining cells, LRCs). These experiments led to the widespread notion in the field that the SCs would be found among the LRCs. However, a number of key questions have remained unanswered, such as: Where exactly do the SCs in the tooth reside? How are SCs able to produce the correct number of ameloblasts to keep the tooth at a fixed length? How do SCs react to injuries such as trimming? Is there only one type of SC, or are there several discrete populations with variable functions?
Our adventure started back in 2013, when Allon Klein (klein.hms.harvard.edu) visited UCSF to share the quantitative approaches that his lab at Harvard was taking to study SC behavior. We realized immediately that these methods would be valuable for the mouse incisor field. The large, single-output incisor niche provides a contrast with the multiple, small, independent units found in other well-studied epithelial systems, such as the hair follicle and gut crypt. As such, it offers a rare opportunity to study the mechanisms that regulate cell number, as we can count the output of all of the SCs into the entire organ. The large size of the niche also offers the chance to study the unique challenges of maintaining proper SC numbers; for example, if a few crypts or hair follicles are lost, it is not a catastrophe for the animal, but loss of even one incisor SC niche would be fatal in the wild. Also, because teeth are largely dispensable for the survival of laboratory-housed mice, we can injure them and study their regeneration over extended periods without killing the animal.
At least initially, we assumed that our undertaking would involve a relatively straightforward analysis, as we thought that we knew the location of the SCs and their proliferation dynamics, and we had in our hands several inducible genetic tools for in vivo lineage tracing of these cells. As with many scientific adventures, however, things were more complicated than they seemed at first.
Historically, a classical view in the SC field in general has been that tissue SCs are slow cycling LRCs, and this was also true in the incisor field. While this view has evolved over the last decade, it is still often thought that SCs cycle less frequently than their immediate progeny, typically known as transit amplifying cells. In previous studies conducted by our lab and others, we pulsed mice with 5-bromo-2′-deoxyuridine (BrdU) to label proliferating cells during the early postnatal period and then aged the mice for several weeks. Cells within the presumed SC region at the base of the tooth, called the outer enamel epithelium, acquired BrdU label and retained it for several months. The extended retention of the BrdU label was interpreted to mean that these cells are SCs. However, to our surprise, when we pulsed 8-week-old mice with BrdU, the outer enamel epithelium was not labelled at all (Figure 2). No matter how much BrdU (or EdU, 5-ethynyl-2′-deoxyuridine) we used, the cells in the LRC region that we had presumed to contain an active SC population remained unlabeled! Lack of cell cycling in this region did not initially make sense to us, because the incisor fully turns over every 4-6 weeks, and therefore, we predicted the cycling time of the SCs to be relatively short — similar to that in other fast-cycling tissues, such as the epidermis and the intestinal epithelium.
Figure 2: Schematic models showing broadly distributed cycling cells in the incisor growth region when mice are pulsed at the perinatal period (left). Cells within the outer enamel epithelium acquired BrdU label and retained it after 7 weeks. In contrast, when mice are pulsed at 8 weeks of age, cycling cells are absent from the outer enamel epithelium (right).
The dramatic transition from active proliferation to a dormant outer enamel epithelium over the first few weeks of the mouse’s life prompted us to define the time period during which the incisor is in steady state. To this end, we performed a series of 3D micro computed tomography (µCT) analyses of the incisor during postnatal development. We found that the incisor SC niche, called the labial cervical loop, is in steady-state between 8 and 16 weeks of age. During this period, the incisor growth rate is minimal, the cervical loop size is constant and proliferation is stable. We reasoned that the LRCs identified in previous incisor experiments represent post-mitotic cells that proliferate only during the postnatal expansion period, and we therefore focused our analysis on the steady state period.
Some cells must be cycling to incorporate a BrdU or EdU label. Since cells were not cycling in the outer enamel epithelium during our desired nucleotide pulse period, we decided to instead use an H2B-GFP label dilution system. In such a system, expression of inducible or repressible H2B-GFP, driven by a tet-response element, is controlled by a tetracycline (Tet)-transactivator (rtTA in Tet-On or tTA in Tet-Off). The proliferation dynamics of the organ can be tracked, because during the chase period, the resulting GFP is diluted by half during each cell division (Figure 3).
Figure 3: Schematic models showing the inducible and repressible H2B-GFP systems. In a tetracycline-inducible (tet-off) double transgenic mouse system (top), constitutive GFP protein expression is shut off by treatment with doxycycline, while in the repressible (tet-on) system, doxycycline administration turns on GFP expression (bottom).
We initially used a K5tTa;tetOff-H2B-GFP, in which constitutive GFP protein expression in the incisor epithelium can be shut off by treatment with doxycycline1. However, we soon discovered that the incisors of the tetOff-H2B-GFP mice were abnormal: they were smaller, had cracks and frequently broke in our hands while we prepared them for analysis, and appeared chalky white (in mice, this is not a sign of good tooth hygiene, but rather an indication of lack of mineralization). Our μCT analysis confirmed that indeed there was a significant decrease in incisor volume and enamel density. So, this line could not be used to determine incisor cell kinetics (anomalies of other organs, such as the cornea, have been noted by others2, which perhaps is due to very high GFP levels in early life that are destructive to some organs). We then switched to the repressible TetOn-H2B-GFP line, in which H2B-GFP is activated by doxycycline treatment3. To our relief, the incisors of these mice appeared normal. However, again with this line, the outer enamel epithelium was devoid of labeling, unless we pulsed the mice during the perinatal period. A similar lack of cell labeling in other tissues, such as the olfactory bulb and the spinal cord, has been noted by the researcher who developed the line and was attributed to an inability of doxycycline to cross the blood-brain barrier4. In our case, we think that the cells are not labelled in the incisor because they are so quiescent that they don’t replace unlabeled histone H2B with the labeled one.
We decided to stick with the TetOn-H2B-GFP line, in which we were able to label the active region of the incisor epithelium and asses proliferation dynamics using a short pulse of doxycycline. We first used flow cytometry to sort the epithelial cells during a chase period and measure their GFP intensities, and then we modeled how the GFP signal distribution should change between chase day 1 to chase day 7 to infer two parameters: the fraction of cells that are proliferating, and their division rate. The model we specifically considered assumed that the number of divisions of any individual cell was Poisson-distributed, which gave a very good fit to the data. The best fit was when the average number of divisions in six days was around 3 (2.98 ± 0.20), and that initially 60±15% of cells were post-mitotic.
While the sorted GFP results provided us with valuable information regarding proliferation dynamics in the incisor epithelium, these data did not tell us anything about the spatial distribution of division. We decided to investigate the proliferation dynamics of the entire organ in situ. Using a two-photon microscope, we acquired images of the entire proximal region of the incisor at 45 minutes and 48 hours after EdU injections. We chose 48 hours, because at this time-point the cells have divided once on average, and no cells have yet been lost due to distal movement along the incisor length. It was clear from looking at the images that many EdU cells moved from the active proliferative region to other areas within the cervical loop. However, quantitative analysis of the location and level of EdU in each cell remained a challenge. This was especially true in our enormous region of interest, which contained about 175 images of 900 µm (length) X 900 µm (height) X 350 µm (width), with very densely packed cells and variabilities in laser intensity, due to the need to penetrate deeper sections of the sample. At this point, we were introduced by our colleague Jacqui Tabler to Kyle Harrington (kyleharrington.com), an expert in image analysis from the University of Idaho, who ultimately developed a segmentation pipeline tailored to our needs.
The software that Kyle developed uses a region competition algorithm to express image intensity and statistics of candidate regions as energy terms that are iteratively refined and balanced until the segmentation converges. After filtering regions based upon the expected size of cells, it measures the EdU signal at the center of the cell to determine proliferation status (Figure 4). The result of this image analysis pipeline was a 3D map of the spatial distribution of individual cells and their corresponding EdU signal, which confirmed our observations from the thin sections and fit well with our single cell RNA sequencing data regarding the sites of active cell division and the flow of cells from these sites to other regions in the incisor epithelium.
Figure 4: An example of the segmentation pipeline shown in video which was rendered in the freely-available ImageJ-based tool, SciView5. Cell membrane labeled in magenta and the center of segmented EdU+ cells shown in yellow.
In this blog post, we have shared several aspects of our thought process during our recently-completed project, in the hope that other researchers will find it useful when planning similar experiments or facing unexpected outcomes in their analyses of epithelial SCs. In our recent paper6, we integrate our quantitative proliferation kinetics with unbiased single cell RNA-seq analysis and genetic lineage tracing. We were able to uncover cell behaviors that upended the reigning dogma about the identity, location and function of progenitor cells in our tissue of interest.
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