The Rieger lab is searching for a postdoctoral scholar in the Biology Department at the University of Miami.
The postdoctoral scholar will be working on mechanisms of chemotherapy-induced peripheral neuropathy using primarily zebrafish as a model system. Chemotherapy-induced peripheral neuropathy is a side effect of chemotherapy that affects 60-70% of patients for which currently no cures exist. The Rieger lab has developed a zebrafish model with which to study the mechanisms underlying sensory axon degeneration due to one particular chemotherapeutic agent, paclitaxel. Studies in this model identified a target, MMP-13, which when inhibited alleviates neuropathy. The goal is to analyze the role of paclitaxel in MMP-13 dysregulation and axon degeneration. This project involves in vivo imaging of zebrafish. A second goal is to collaborate with medicinal chemists to develop new inhibitors and test them in zebrafish and rodents in preparation for clinical studies in collaboration with the University of Miami Medical School.

Requirements:

Candidates must have a Ph.D. in Biology, Neurobiology, or a related discipline, and experience in conducting research with zebrafish, molecular biology and confocal imaging. Other important qualifications are a background in statistical methods, strong work ethic and strong organizational and time management skills. Other qualifications include problem-solving skills, experience in manuscript writing, a track record of publications, and independent research experience. Knowledge of medicinal chemistry or drug development are preferred.

Apply online at:

https://umiami.wd1.myworkdayjobs.com/en-US/UMCareerStaff/job/Campus—Coral-Gables/Post-Doctoral-Associate_R100025704-1

Position # P100041395

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Hello, I’m Alex and I’m very happy to introduce myself as a new Reviews Editor for Development. I will be working together with invited authors to produce interesting and relevant reviews, opinion pieces and posters for our community. It’s really exciting to be at the forefront of research!

Before joining the Company of Biologists, I had a short PostDoc with Prof Phil Ingham at the Living Systems Institute in Exeter. My project focussed on understanding the evolution of Hedgehog pathway to explain divergent signalling mechanisms in different species using the sea anemone, Nematostella vectensis, and it was here that I developed a keen interest in EvoDevo research. Before that I was a PhD student with Prof Jim Smith at the Francis Crick Institute/NIMR in Mill Hill, where I investigated cardiovascular development in the zebrafish.

Although I’ve hung up my lab coat for the last time, I’m thrilled developmental biology will remain a big part of my life and that I’ll get to stay within the community. Thank you to the team at Development and the Node for their warm welcome and I look forward to working together with you in the future. Please feel free to connect with me―my contact details are below. You’ll see from my Twitter that I like to post about zoological oddities, sloths and allotments!

Best wishes,

Alex

Twitter: @amjeve
LinkedIn: amjeve

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We have an opening for a motivated postdoctoral fellow with strong Drosophila molecular genetics experience and interest in post-transcriptional regulatory biology to join our team.

Although miRNAs are often thought to mediate “fine-tuning”, we revealed many examples of profound defects in neural miRNA mutants. These include switches of cell fates in diverse types of sensory organs and in the CNS, and disruptions of neural function and behavior.

We are particularly interested now to exploit precise CRISPR engineering of 3’UTRs of critical targets, to dissect target regulation in specific celltypes and circuits that underlie phenotypes, and integrate this with genomic approaches, including single cell RNA sequencing. Moreover, our interests in miRNAs  dovetail with our studies of other modes of post-transcriptional regulation that are critical in the nervous system, including alternative polyadenylation, splicing, and RNA methylation.

The ideal candidate will be comfortable with classical and modern fly genetics and molecular methods, but will be interested to integrate biochemical, genomic, and/or computational strategies utilized by other lab members in a multidisciplinary and interactive environment.

Generous compensation, benefits, and housing package is available immediately (https://www.mskcc.org/education-training/postdoctoral/current-incoming). Please provide CV, motivation letter and 3 references to Eric Lai, laie@mskcc.org.

https://www.mskcc.org/research/ski/labs/eric-lai

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Welcome to our monthly summary of developmental biology (and related) preLights  

In our last post of the year, we again have plenty of exciting research to feature, and would like to thank all the preLighters for their incredible work selecting and highlighting interesting preprints in a broad range of topics for the community. Early next year, we have two new ‘experiments’ coming at preLights, so stay tuned!

Morphogenesis

Sundar Naganathan wrote about endoderm morphogenesis in Drosophila and how local transcription activation of myosin and coupled mechanical changes regulate it. Staying with this model organism, Ivana Viktorinova’s preLight questioned whether the century-old Hertwig’s rule (stating that cells divide along their long axis) is universally valid. The authors of the study showed that in a population of the epidermis this is not the case, and that the tension exerted by actomyosin contractility – rather than the length of the axis – determines orientation of cell division.

The phenomenon of cell competition was initially described in Drosophila – in 1975 – and since then a number of its molecular details have been unravelled. This month, Rohan Khadilkar reviewed the inter-relationship between infection, the Toll-pathway induced innate response and cell competition. Sarah Bowling and Teresa Rayon explored a new role for this process during early mouse development, where unspecified cells are competitively eliminated during epiblast formation.

Moving to zebrafish, Teresa Rayon wrote about scaling of morphogen gradients and a preprint studying how in embryos of varying sizes the patterning of the neural tube remains proportional. The authors found that Shh and a protein required for Shh release – Scube2 – form a feedback loop that allows proportional patterning. From the world of plants, an intriguing kind of growth pattern, root circumnutation, and its regulation by a  novel histidine kinase featured in the preLight by Martin Balcerowicz.

Figures taken from preprints by Hashimoto & Sasaki and Collins et al.,preLighted by Teresa Rayon and Sarah Bowling

From Development to Disease – scRNAs-seq studies in the spotlight

Sequencing the transcriptomes of individual cells in a tissue or organism has become a powerful way to gain new insights into development and disease and build valuable resources (watch out for the journal Development’s next special issue). A preLight by Leena Rasrado reported on mouse neural tube development at the single-cell level: with scRNA-seq, the authors unravelled the molecular mechanisms underlying spatial and temporal neuronal diversity.

Rob Hynds highlighted a study on the differentiation of the respiratory epithelium by scRNA-seq. In another preLight, he covered how the analysis of scRNA-seq datasets from cancer cells can identify specific targets of T cells for immunotherapy. Hannah Brunsdon often highlights cancer studies in model organisms, this month she featured work from bioRxiv’s ‘confirmatory results’ category where two groups validated and compared zebrafish models of B cell acute lymphoblastic leukemia.

The preprint Shikha Nayar wrote about combined single-cell transcriptomics with other ‘omics approaches to show that old fibroblast cultures are heterogeneous, and contain a larger proportion of ‘activated fibroblasts’. Remarkably, these cells secrete cytokines, which result in higher reprogramming efficiency.

Finally, Samantha Seah covered two preprints that made important technological advances in droplet-based single-cell sequencing. The two new methods, RAGE-seq and DART-seq allowed researchers to capture sequence diversity at the 5’ end of a transcript and thus study the diversity of B-cell and T-cell receptors.

Figure taken from preprint by Singh et al., preLighted by Samantha Seah

Gene regulation & Chromatin

Genome-wide association studies have been useful in linking genetic variants (mostly SNPs) to diseases, however the majority of these SNP-s fall into non-coding regions with potential regulatory functions. Jesus Victorino preLighted work from the van Steensel lab assessing on a genome-wide scale which SNPs can modulate expression of a reporter. Carmen Adriaens and Clarice Hong teamed up to highlight another preprint from this lab, which used massively parallel reporter assays to study features of gene repression in heterochromatin at the nuclear periphery. Heterochromatin was also at the centre of Gabriel Aughey’s post, which discussed how cell cycle speed during early embryo development in Drosophila sets the pace for its establishment.

Visit the preLights website for much more – such as a highlight from Theo Sanderson about an emerging field of optics-free microscopy, or Ashrifia’s post on how favourable environmental conditions lead to an increase in languages spoken in a specific geographic area.

(1 votes)

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Two postdoctoral positions are available in the Boavida laboratory in the Department of Botany and Plant Pathology at Purdue University. The lab investigates the molecular mechanisms controlling gamete identity and function and cell-cell signaling in plant fertilization.

We are seeking highly motivated individuals who value achievement, are proactive, creative, detail-oriented, flexible and constructive team players. Candidates should have completed their Ph.D. degree on a relevant area in Biological Sciences within the last 2-3 years. Proven ability to present and publish research data and excellent English communication skills both oral and written are required.

Postdoc A. The recruited researcher will work in two main axes of investigation based on (1) identification of interaction partners of gamete-expressed proteins primarily through a high-throughput proteomic screening (NGS-Y2H); and (2) integration of PPI networks with derived NGS-transcriptomic approaches to establish high-confidence interaction maps for prediction and functional characterization of signaling networks operating in plant gametes and fertilization. The ideal candidate should have experience in molecular biology and protein biochemistry (membrane protein expression and purification from various cellular systems, Y2H, BIFC, co-immunoprecipitation, etc.). Knowledge of integrative computational analysis, structural bioinformatics, protein docking and characterization of regulatory networks is an asset.

Postdoc B. The postdoctoral researcher will use an exciting combination of techniques including flow cytometry (Microfluidics and FACs), genomics, transcriptomics, epigenomics, and molecular biology to study mechanisms underlying cell identity and differentiation of plant gametophytes in a spatiotemporal context. He/she will also be involved in the characterization of a cell-based functional assay to study the cellular functions of candidate genes in plant fertilization. Prior experience in flow cytometry, confocal microscopy, molecular genetics, functional genomics, and bioinformatics is highly desired. Candidates with interest in sexual reproduction and/or documented experience in Arabidopsis, tomato and maize are especially encouraged to apply.

Application

Applications will be accepted until the position is filled and should include CV, contact details of at least 2 references and a cover letter outlining how your previous research experience/contribution integrate with the laboratory research projects. Incomplete applications will not be considered for evaluation. Relevant publications and information can be found here.

Please send your application as a single PDF file to lboavida@purdue.edu. Include in the subject header PlantRepro2018_A (or B) (according to your interest in Postdoc position A or B).  The appointment is initially for 12 months with the possibility of extension depending on research performance and available funds. Starting date is flexible and can be as early as February 1^st, 2019.

Purdue University is an Equal Opportunity Employer, has a strong commitment to diversity and actively encourages applications from candidates from groups underrepresented in higher education.

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A PhD position is available in the laboratory of Néva Meyer at Clark University in Worcester, MA USA. Research in Dr. Meyer’s lab is currently focused on understanding how the central nervous system develops in annelids with the goal of gaining a better understanding of how nervous systems evolved.

The successful applicant will develop a project focused on molecular control of neural fate specification in the annelid Capitella teleta, but this can be expanded to include other spiralians and different avenues of research depending on the applicant’s interests and goals. Additional information is available on the Meyer lab website.

The anticipated start date is late August 2019. Previous experience in molecular biology and working with marine larvae and/or bioinformatics is desirable. The successful applicant will be guaranteed funding for five years through a combination of research assistantships and teaching assistantships; two years of research assistantship for this position are currently available.

Please email a cover letter explaining your interest in the position and qualifications and a CV to: nmeyer[a]clarku.edu.

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When we examined the kinetics of Rho GTPase activity in endothelial cells in response to receptor stimulation (Reinhard, 2017), we noticed considerable cell-to-cell heterogeneity. In the original work we published graphs with the average response, reflecting the response of the whole cell population. However, these graphs fail to show the cellular heterogeneity. What is the best way to visualize these data? I don’t know, but I will explore several options to visualize all individual traces from the experiment.

1. All together

The original paper displayed the average responses of the Rho GTPases RhoA, Rac1 and Cdc42 and the graphs were remade with R/ggplot2 in this blog. Since the Rac1 data showed the largest differences between cells, I took this data to explore different ways of visualizing it. One can simply plot all the individual lines together with the average response in a single graph (figure 1). How this can be achieved with R/ggplot2 is explained in this blog. The graph may look cluttered when there are many lines and therefore it is advisable to use thin gray lines. To enable the identification of individual traces one may use a unique color for each trace. Putting all traces from a single experiment in the same graph is a simple but effective method to depict variability. However, putting all data in a single graph for multiple conditions is usually makes the graph difficult to understand.

Figure 1: Ordinary graphs showing the data from individual cells as thin lines on top of each other. In the left panel the average is shown as a thicker black line. In the right panel the data from each line has a unique color to improve identification of cells.

2. Ridges

Instead of putting all graphs on top of each other, an offset along the y-axis is introduced, which reduces overlap. This type of plot is called a ridgeline plot (formerly known as a joyplot). By adding an increasing offset for the x-axis, the curves are further separated and a 3D effect is generated. This may result in a nicer plot to look at, but it comes at the cost of impeding quantitative interpretation. For example of the use of ridgeline plots fro kymographs see this blog.

Figure 2: Ridgeline plots showing the individual cellular responses with an offset in the vertical direction (left) or an offset in both vertical and horizontal direction (right).

3. Small multiples

Instead of plotting all lines in a single graph, it is also an option to show all lines in individual plots. This type of design is coined the “small multiple” and it was popularized by Edward Tufte. The presentation of small multiples is not limited to line graphs, but be used to depict other types of data, e.g. several still images from a movie.

Small multiples can be reproduced in R/ggplot2 by using ‘faceting’ and this has been briefly highlighted in a previous blog (near the end). Figure 3 (left) shows the standard output of ggplot2, with labels for each plot. In my opinion, the small multiple is at its best when it is trimmed down to the bare minimum. As an example, I made a minimalistic variant with a high data-ink ratio (figure 3, right). Although numbers are absent, this visualization still allows for quantitative comparison and it shows the heterogeneity. In summary, the small multiple is a great way to depict the individual data, but it requires quite some space.

Figure 3: The small multiple presentation of the time-lapse data shown as the standard output of ggplot2 with facets (left) and a minimalistic version (right) with improved data-ink ratio.

4. Heatmap

The ‘heatmap’ is well-known for the visualization of microarray data. It can also be used for different types of data including time-series data. To generate a heatmap, the actual numbers of each of the line graphs are color coded and these are presented as a line. Every line (row) of the heatmap represents a single trace from a time series experiment. The result is an image in that shows the responses encoded by colors (figure 4).

As the data is encoded by color, the quantitative comparison of the data is limited to comparing colors. This comparison is inherently non-quantitative. As such, the downside of a heatmap is that it only allows qualitative interpretation. Still, heatmaps enable the visualization of a lot of data on a relatively small space.

Figure 4: Heatmap style presentation in which each row represents the data from a cell. The response is color coded according to the legend. The data is presented in order of acquisition (left) or clustered based on similarity (right).

Sorting out the mess

The data from single cell experiments can be very heterogeneous (as in this example) and may look like a mess. To structure the data, it can be sorted or grouped. Sorting uses a feature of the data that can be used to rank the individual traces. For instance, for the ridgeline plot (figure 2) the maximum response of every trace was determined. Subsequently, the plots were ordered based on the maximum response (ratio) that was observed. Any other parameter can be used to order the data, e.g. integrated response, timing of the maximum response or noise of the baseline.

Another method to structure the data is grouping or clustering. For the data presented in the heatmap a hierarchical clustering was performed (figure 4, right), which determines the pairwise difference for each of the traces. The ouctome is an order of traces that groups similar responses together. There are many ways to cluster data and information on this topic can be found elsewhere (for instance here, here and here). In the end, the sorting or clustering of the time-lapse data may structure the data and aid interpretation. It can precede any of the visualizations shown above, but it makes most sense for ridgeline plots or heatmaps.

Howto

This blog explores several options to visualize data, but does not go into detail on how to achieve it. The code (and data) that was used to generate the graphs is available at Zenodo (http://dx.doi.org/10.5281/zenodo.2211123) and should give some idea of how to achieve the different visualizations in R/ggplot2. Alternatively, some of the plots shown in this blog (including small multiples and heatmaps) can be made with a web-based tool PlotTwist that uses R/ggplot2 but does not require coding skills. A nice feature of the web tool is that the presentation of the data (e.g. visibility of the lines and their color) can be adjusted in realtime.

Final words

Showing all of the data is important for full appreciation of experimental results.  Since cell-to-cell variability is observed in every single imaging experiment, methods to visualize the heterogeneity are needed. Here, I have highlighted several methods to visualize data from time-lapse imaging. These methods may be combined with some way of sorting or clustering of the data. Which approach works best for your data needs to be examined on a case-by-case basis and will depend on what you want to communicate. The different visualizations shown here may serve as a starting point to explore how to best visualize your data from time-lapse experiments.

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