Hi Everyone,

I thought I would post our travel award for life science researchers which might be of interest.

At Antibody Genie we run a quarterly travel award for Life Science researchers, all you need to enter is to write an 800 word blog piece on your research area or write a piece on your life as a PhD/Post-Doc/PI.

For entrants that blog on their research area we also ask them to submit a diagram to explain their pathway/structures etc. We redesign these diagrams on posts them on the site.

In our last round we had 20+ entrants, with two researchers invited to international conferences based on promotion that we did on their blog posts.

You can find out more information here:

https://www.antibodygenie.com/travel-award/

Best of luck

Seán

(2 votes)

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The process of subdividing a tissue into functional units represents a classic problem in pattern formation. Signalling proteins – so-called morphogens – orchestrate this process. The traditional view is that morphogens are released from local source and slowly diffuse through a neighbouring tissue to build up a gradient. As Wnt signals act as a key morphogen in tissue patterning, it is believed that similarly these signal proteins diffuse long range to exert their morphogenetic function.

However, we have recently identified long signalling filopodia – so called cytonemes – that tightly control transport of Wnt proteins. We have observed fast and directed distribution through expanding tissues. It is unclear how such a dissemination generates a stable and robust signalling gradient.

The two fully-funded PhD project will focus on:

(1) deciphering of the molecular mechanism of Wnt transport,

(2) simulating of the impact on patterning,

(3) validating the prediction in growing tissue

(4) comparing Wnt trafficking in healthy and diseased tissue

The Scholpp lab in the Living Systems Institute (LSI) Exeter is an optimal environment to conduct these doctoral training studies. The LSI offers unique training opportunities for the PhD student as it allows the student address key problems in life sciences with state-of-the-art equipment in an interdisciplinary environment. The centre facilitates interaction between empirical and theoretical scientists leading to the development of predictive modelling capacity from experimental data, leading to accurate, mechanistic descriptions of biophysical processes for entire ‘living systems’. The project includes close collaboration with the universities of Bristol and Cardiff to complement the required skill sets.

Application deadline: 24.11.2017

Start date: Sep/Oct 2018

Detailed information can be found here:

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The López-Schier laboratory at the Helmholtz Zentrum Munich in Germany is seeking creative and highly motivated PhD students or postdoctoral scholars to work within our group of 9 graduate students and postdoctoral fellows. The working language of the laboratory is English.

Our group focuses on understanding the development, regeneration and function of sensory systems and neuronal control of metabolism. We use the zebrafish as experimental model, and integrate molecular, cellular, behavioural and clinical data. We also have developed new technical approaches to understand organogenesis, including cell-fate acquisition after regeneration from tissue-resident progenitor cells. Mutations in many of the genes that we have identified are responsible for neurological diseases and cancer. Our ultimate goal is to connect our studies of fundamental mechanisms to understand health disorders in humans.

We currently have 4 fully funded openings for the following projects:

1. Cellular and genetic bases of organogenesis, including cell packing and tissue remodelling. This project combines single-cell transcriptional profiling, genome engineering using CRISPR/Cas9 and quantitative live imaging data by light-sheet microscopy. Preference will be given to candidates with theoretical or practical knowledge in cell biology or biophysics.

2. Control of cell number, organ size and proportions. Using state of the art high-resolution cell tracking, optogenetics, genome engineering and machine learning, we attempt to understand how cells self-organize and to predict cellular behavior during the regenerative response after tissue injury. This project is ideal for a candidate with a background in physics or engineering and a good command of computer programming.

3. Neuronal control of metabolism. This project combines transcriptional profiling, genome engineering and neuronal-activity imaging by light-sheet microscopy to understand the neuronal basis of systemic metabolism, and neuronal dysfunction in metabolic syndromes. Preference will be given to candidates with theoretical or practical knowledge in neuroscience, electrophysiology and/or optogenetics.

Qualifications & skills

– University studies in biology-related sciences, physics, engineering or computer science (PhD)

– Ideally having recently completed or about to complete a PhD (Postdoctoral)

– Having published or likely to publish at least one first-author paper in a

first/second tier journal (Postdoctoral)

– Candidates for all position should have a strong inner drive, independence, and willingness to work in a highly interdisciplinary team

– A good command of the English language is essential

Laboratory

The team’s projects are interdisciplinary, and are aimed at understanding the basic rules that allow sensory systems to develop, regenerate and function. We use confocal, spinning-disc and light-sheet microscopy imaging, biochemistry, genome engineering by CRISPR/Cas9, laser nanosurgery, optogenetics, and machine learning.

Environment

The Helmholtz Zentrum in an innovative, well-equipped and scientifically stimulating élite research centre located in the outskirts of Munich, one of the most attractive and innovative major cities in Germany. Situated at the foothills of the Alps, Munich is a cosmopolitan city that has ranked among those with the highest quality of life in Europe.

Contact

Please, apply via electronic mail only, including a cover letter with a short statement of research interests and motivation, a Curriculum Vitae including a list of names and email-addresses for two/three academic references, to:

Dr. Hernán López-Schier

Research Unit Sensory Biology & Organogenesis

Helmholtz Zentrum München
Ingolstädter Landstrasse 1
85764 Neuherberg – Munich, Germany

E-mail: hernan.lopez-schier@helmholtz-muenchen.de

Website: http://www.gsn.uni-muenchen.de/people/faculty/associate/lopez-schier/index.html

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Structuring data according to the ‘tidy data‘ standard simplifies data analysis and data visualisation. But understanding the structure of tidy data does not come naturally (in my experience), since it is quite different from the structure of data in spreadsheets or tables. Here, I explain how to convert typical spreadsheet data to tidy data to facilitate data visualisation.

When I started to use ggplot2 for data visualisation, I was impressed by its power and elegance. With ggplot2, a free, open source package for R, you can make high-quality graphs. For instance, the graphs that I made for a previous blog advocating the importance of showing the actual data instead of summaries were made with ggplot2 (scripts available here). Before I could make those graphs, however, I was struggling with the tidy data structure, which is a requirement for using ggplot2.

The reason that I was struggling is that as a researcher I am used to collect, process and summarise my data in spreadsheets. In addition, I am used to read and present data in a tabular format. Tidy data contains exactly the same information as non-tidy, spreadsheet data, but it is organised in a different way. The tidy data standard defines a consistent way to structure datasets, facilitating data manipulation and visualization (Wickham, 2014). So, once the data is organised according to the tidy data principles, making the graphs is relatively straightforward.

Below, I will explain how data in the typical spreadsheet format (and I’m not naming names here) can be converted to tidy data using R. I will use two examples of data in a spreadsheet format that I often encounter during my research. The first example is a dataset in which a single parameter is compared across different conditions. The second example is a dataset from a time-series experiment with different conditions.

Before we can perform the conversion to the tidy format, we need to define what types of information are stored in a dataset. I will use the nomenclature that is used in Tidy Data by Hadley Wickham. Let’s say that you have measured cell lengths under a number of different experimental conditions (A,X,Y,Z). An example dataset in the typical non-tidy spreadsheet format would look like this:

A     X     Y     Z
0.8  7.1  9.9  5.1
1.9  6.7  9.5  4.5
2.6  6.3  8.5  4.4
3.6  6.2  8.7  4.3
4.4  4.5  7.3  3.3
5.7  4.3  6.2  3.1
6.3  4.1  5.5  2.5
7.1  4.8  6.8  3.4
8.2  5.1  7.3  3.6
9.2  5.5  7.9  3.9

The dataset consists of values and these are usually numbers or text. In this dataset, all the measured values represent the same characteristic (cell length) and therefore belong to a single variable. Here, we indicate that variable as  “Length”. Since the values that represent cell length are experimentally determined, “Length” is a measured variable. The different conditions (A,X,Y,Z) are also assigned to a variable; “Condition”. Since this variable was known when the experiment was designed it is a fixed variable.

The most important requirement for tidy data is that each variable occupies only a single column. This rule is violated in the example above, because values that belong to the same variable (“Length”) are distributed over different columns. The tidy version of the dataset would have two columns, one with the fixed variable “Condition’ and one with the measured variable “Length”. Below, I will explain how to use R to convert the spreadsheet data into tidy data.

In the next part, which will be a tutorial on tidying data in R, I assume some basic knowledge of R, including setting the working directory and installing packages. The packages that need to be installed are tidyr and ggplot2. The example dataset is available here. 

Example 1: Tidying a spreadsheet with different conditions

First, we will read the non-tidy spreadsheet data from the file ‘test-data.csv‘ and assign the data to a dataframe named ‘data_spread’

> data_spread <- read.csv('test-data.csv')

To verify this step, you can print the first six lines of the dataframe by using the function head() with the name of the dataframe as the argument:

> head(data_spread)

Which will return:

    A   X   Y   Z
1 0.8 7.1 9.9 5.1
2 1.9 6.7 9.5 4.5
3 2.6 6.3 8.5 4.4
4 3.6 6.2 8.7 4.3
5 4.4 4.5 7.3 3.3
6 5.7 4.3 6.2 3.1

The first row of the dataframe is the header, which specifies the experimental condition of each column (A,X,Y,Z). All other rows contain the observed cell length.

To convert the dataframe to a tidy format I use the function gather(), which is part of the tidyr package. Using gather(), I specify that “Condition” is taken from the header and should go in the first column and that all the values (which are all cell lengths) should be gathered in the second columns and that this variable should have the name “Length”. The result is assigned to the dataframe ‘data_tidy’:

> data_tidy <- gather(data_spread, Condition, Length)

To show the contents of the entire dataframe, enter the name of the dataframe at the prompt (the result is not shown here):

> data_tidy

To show the first six lines of the tidy dataframe ‘data_tidy’, use the function head():

> head(data_tidy)

  Condition Length
1         A    0.8
2         A    1.9
3         A    2.6
4         A    3.6
5         A    4.4
6         A    5.7

To show the last six lines of the tidy dataframe, use the function tail():

> tail(data_tidy)

   Condition Length
35         Z    3.3
36         Z    3.1
37         Z    2.5
38         Z    3.4
39         Z    3.6
40         Z    3.9

With the new, tidy dataframe ‘data_tidy’ it is straightforward to plot the data with ggplot2:

> ggplot(data_tidy, aes(x = Condition, y = Length)) +
     geom_jitter(position=position_jitter(0.3), cex=1, color="grey40")

Wrap-up
In this example we have converted a spreadsheet with measurements, taken under different conditions, into tidy data. In the tidy data structure, only two columns are used, one for the fixed variable “Condition” and another one for the values that belong to the measured variable “Length”. This format, that looks like a list, may seem odd. It also uses more storage space (or memory), since the “Condition” is listed for every value. Still, this format makes perfect sense in R and it can be used to plot the data with ggplot2.

Example 2: Tidying a spreadsheet with time-dependent data and different conditions
Let’s take this a step further with the same non-tidy spreadsheet data “data_spread”. Now, suppose that the data is from a time series and column A represents time. To indicate that column A is actually “Time” you can change the name of the first column using the function colnames():

> colnames(data_spread)[1] <- "Time"

You can verify that the name of the first column has been changed by using the function head():

> head(data_spread)

   Time   X   Y   Z
 1  0.8 7.1 9.9 5.1
 2  1.9 6.7 9.5 4.5
 3  2.6 6.3 8.5 4.4
 4  3.6 6.2 8.7 4.3
 5  4.4 4.5 7.3 3.3
 6  5.7 4.3 6.2 3.1

In this dataset cell length has been measured at different times and for different conditions (X,Y,Z). In addition to the variables “Length” and “Condition” a third variable “Time” is present in this dataset. Again, this dataset is not tidy since values that belong to the same variable (cell length) are spread over different columns. Note that the first column contains only values that belong to the variable “Time”, which should remain like that. So, for this dataset we want to gather all the values that represent cell lengths in one column, but these should not be mixed with the variable “Time”. To achieve this, we will use the same function gather() and add that “Time” should be excluded from gathering:

> data_tidy_time <- gather(data_spread, Condition, Length, -Time)

Let’s check the structure of the new dataframe:

> head(data_tidy_time)

   Time Condition Length
 1  0.8         X    7.1
 2  1.9         X    6.7
 3  2.6         X    6.3
 4  3.6         X    6.2
 5  4.4         X    4.5
 6  5.7         X    4.3

The new dataframe ‘data_time_tidy’ can be used as input for ggplot2 to plot a “Length” versus “Time” plot for three conditions:

> ggplot(data_tidy_time, aes(x=Time)) +
    geom_line(aes(x= Time, y=Length, color=Condition), size=0.5, alpha=1)

Wrap-up
In this example we have converted a spreadsheet with time-series data in tidy data. The tidy data structure consists of three columns for the three variables “Time’, “Condition” and “Length”. Each column contains only values that belong to the indicated variable. This long format with the values for “Time” repeated several times look atypical. But this format offers flexibility, for instance when the measurements for the conditions are taken at different times. In addition, it is the only structure for packages that use tidy data as input (tidy tools). Finally, the tidy data structure enables plotting the data with ggplot2 and grouping the data or coloring the data according to the variable “Condition”.

Final words
If you do not comprehend the concept of tidy data right away, don’t worry. It took me actually quite a while to grasp it and I’m still not confident that I fully understand it. I can highly recommend reading the paper Tidy Data by Hadley Wickham, which offers a thorough and clear explanation. In my experience, the best way to learn how to tidy your data is by doing it. Start out easy, with the example dataset, or with some ‘simple’ data of your own. From there on, you can work on more complicated datasets. Many examples of tidying more complex data are out there, including a very nice tutorial on Data Tidying by Garrett Grolemund. If you are stuck, try to find solutions online, use a community site, ask a colleague (this is the right moment for me to thank Katrin Wiese) or post a tweet. I hope that this tutorial will help people that are used to working with data in spreadsheets and tables to take full advantage of the power of ggplot2, tidy tools and R.

Acknowledgments: A shout-out to the twitter community, to anyone sharing code and Jakobus van Unen, Katrin Wiese and all other colleagues for their input.

(18 votes)

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Reposted from the SDB’s website

The Society for Developmental Biology is deeply concerned about the damage caused by Hurricanes Irma and Maria to the laboratories of our colleagues in Puerto Rico. In order to facilitate the continuation of research programs in those labs or at another temporary host location, SDB is offering relief grants of up to $10,000 to each lab. Funds may be used for replacement of organisms, reagents, supplies, travel to host lab by PI or his/her trainees, core facility usage fees at host institution, etc. For questions, please contact Ida Chow at ichow@sdbonline.org.

Eligibility: Principal investigators (PIs) at institutions located in Puerto Rico who are currently conducting research projects in developmental biology. Priority will be given to PIs who are current SDB members. The PIs may request support for travel to host lab for themselves, as well as for their trainees (students and postdocs) who are active participants in the project.

Deadline: As soon as possible, no later than December 31, 2017. Proposals must be submitted as one complete PDF attachment to ichow@sdbonline.org, and they will be reviewed as they are received.

If you would like to financially support this grant, please donate online here and select “Puerto Rico Relief Fund.”

Find out about the host labs already signed up and how to apply here:

https://www.sdbonline.org/puerto_rico_relief_grant

(3 votes)

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A 3-year DFG-funded PhD-position is available at the Jasper Collaboration Group at the FLI-Leibniz Institute on Aging. Our work focuses on the Drosophila intestine as a model for adult stem cell regulation and aging (http://www.leibniz-fli.de/research/associated-research-groups/jasper/).

In this DFG-funded project, we will focus on elucidating the mechanisms of entero-endocrine (EE) cell differentiation and the role of EE cells in age-related dysbiosis using a combination of genetics, transcriptomics and transcription-factor binding studies. Despite their importance in controlling animal metabolism, behaviour and immune responses, there is still a great deal to learn about how EE cells are formed from intestinal stem cells and the influence EE cells exert on the microbiome, organismal aging and stress responses.

We are looking for highly motivated individuals who are fascinated by stem cells, transcription factors and the genetics of aging. Experience with Drosophila, stem cell systems and/or bio-informatic analysis of NGS-sequencing data is a pre. Please contact Dr. Jerome Korzelius (jerome.korzelius@leibniz-fli.de) about any details regarding this position.

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Well, the same behavior applies with buying antibodies. We look to publications like “reviews,” to ensure antibodies have produced reliable data in similar experimental contexts.

While sifting through publications is the gold standard for finding antibodies, however, there are some limitations. In this article, I’d like to outline these limitations and show how independent antibody reviews could address them.

1. Latency Between Data Generation and Publication

Problem: It takes an average of 9 months from manuscript submission to publication. This delay in data availability due to the (essential) peer-review process could lead other labs to waste valuable resources testing an antibody that has already been validated.

Solution: The submission of independent reviews immediately following successful data generation would ensure that data about successful antibody usage was shared within the research community without delay.

2. Only Positive Results Are Published

Problem: For every beautiful, sharp and crisp IF image, there were probably 2-3 antibodies that were tested and didn’t work. However, such negative data are usually never published and are forever hidden in the hard drive of a lab computer.

Solution: Independent reviews can capture cases where antibodies didn’t work, to help other scientists make more informed decisions—the same way we avoid restaurants with horrific reviews.

3. Validation Data May Not Be Shown

Problem: Most labs have validated antibodies targeting their protein of interest through knock-out or knock-down experiments. Yet the validation data are not always included in the publication because it’s “not part of the story,” and are often only known to the reviewers and lab members.

Solution: The sharing of antibody validation data from every lab through reviews would generate an invaluable database of validated antibodies.

4. Limited Space for Detailed Protocol

Problem: Many journals have word limits for their publications, which in turn caps how much protocol detail you can put into the Materials & Methods. And as we all know, to conduct a successful experiment, even the tiniest detail matters.

Solution: Unbound by editorial constraints, in each antibody review full experimental protocols could be submitted to facilitate reproducibility.

5. Some Data Are Locked Behind Paywall

Problem: Approximately 76% of publications are behind paywall, and thus, the majority of published antibody usage data are not available to all scientists.

Solution: The adoption of independent reviews aligns with the recent movement towards open science, and ensures that every successful usage of antibodies is known to the entire research community.

In fact, we feel so strongly about independent antibody reviews that we’ve actually built them into BenchSci.

At BenchSci, our goal is to drive discovery by ending reagent failure. Our machine-learning technology analyzes open- and closed-access publications and presents published figures with actionable insights. At the same time, we also recognize the limitations discussed in this article, which is why we also incorporated independent antibody reviews into the platform.

With the recent emphasis on research rigor and reproducibility by the NIH, I would like to advocate for the submission of independent antibody reviews to facilitate a collective effort to authenticate key biological resources, which, in turn, would benefit the research community as a whole. If you agree, consider signing up for BenchSci free and contributing reviews.

(3 votes)

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Fully-funded postdoc positions are available in a new lab group starting at the NIH main campus in Bethesda, Maryland. Research in the McJunkin lab has two major long-term goals: 1) to define the biological functions of miRNAs during embryogenesis and 2) to elucidate mechanisms of miRNA turnover. Using C. elegans as a model organism to address these questions, we will combine the strengths of classical forward genetics with CRISPR-Cas-9-mediated genome editing, next-generation sequencing, cell biology, and biochemical techniques. Because embryonically-expressed miRNAs exhibit a sharp decrease in abundance at the end of embryogenesis, our efforts to simultaneously study the biology of these miRNAs and the mechanisms of miRNA decay has the potential to uncover regulatory modules that couple miRNA decay to developmental timing.

The NIH main campus is a vibrant and collaborative research environment boasting over four hundred research groups and an active postdoc community. Bethesda, Maryland is part of the Washington, D.C. metropolitan area, and the NIH main campus is easily accessible by the Washington, D.C. subway system.

Applicants must have completed a Ph.D. within the last three years. Expertise in molecular biology and strong verbal and written communication skills are required. Experience in either RNA biology or C. elegans research is desirable. International scientists and U.S. citizens are equally eligible for these fully-funded positions.

For more information, please see our website (bit.ly/mcjunkin). To apply, please send a cover letter describing which aspect of our research program you are interested to pursue, a CV, and contact information for three references to mcjunkin@nih.gov.

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This interview by Aidan Maartens appeared in Development, Vol 144 Issue 19

Jayaraj (Jay) Rajagopal is a Principal Investigator at the Center for Regenerative Medicine at Massachusetts General Hospital and an Associate Professor of Medicine at Harvard Medical School. A Howard Hughes Medical Institute Faculty Scholar, his lab works on the development and regeneration of the lung. He uses stem cell and animal models to develop novel insights that hopefully will provide inspiration for therapies to help treat human lung disease. He was awarded the Dr Susan Lim Award for Outstanding Young Investigator at the 2017 International Society for Stem Cell Research (ISSCR) meeting in Boston (MA,USA), where we met him to talk about how a fish tank started a life-long fascination with the lung, the transition to running his own lab, and his optimism for the future of both basic stem cell research and its clinical translation.

You’re here in Boston to collect ISSCR’s Susan Lim Young Investigator award: what does the award mean to you?

It really was an incredible honour. I looked over the past recipients and the vast majority of them are either friends or people whose work I admire. Additionally, Susan Lim and her husband Deepak Sharma are just wonderful people – it was great to meet them, and especially their daughter who is interested in becoming a physician. Most of all, it represents a chance to be embedded in a rich community of scholarship that promotes education and younger researchers.

What got you interested in science in the first place?

That started very early for me: I was always intrigued by animals. I spent my summers in India as a child and when all my cousins were in school I’d be out on the farm, capturing animals and things like that. The other early influence was my father, who was a doctor and would talk with me about interesting cases. His perspective was great because he introduced these diseases as fascinating mysteries. For as long as I can remember, those two things – animals and medicine – were what I wanted to devote my professional life to.

How did you become interested in developmental biology and the development of the lung in particular?

I went to college at Harvard and studied molecular biology, and actually specifically avoided developmental biology! Having read The Eighth Day of Creation by Horace Freeland Judson, molecular biology seemed so exciting. I worked with Jack Szostak and Jenn Doudna at the time when self-splicing RNAs were new. We could synthesise the RNA molecules in vitro, put them in a solution and test their enzymatic activity with exquisite precision. The whole process was a lot of fun and appealingly very understandable. Then I went to medical school and loved the human biology and physiology, as well as being able to interact with and treat patients. But at some point I stepped back and asked myself: what do I love? I realised that, at heart, I like cells and tissues, and how they come together functionally to create a living animal, essentially exactly what I was fascinated by as a kid in a more mature guise. Developmental biology just seemed like the natural thing to do.

My interest in the lung also traces back to when I was young. I had a fish tank and even as a very young kid I was mesmerised by it: these animals could breathe underwater, but if you let your tank get dirty – like many kids do – many fish can also breathe air. In medical school I learned about the lung’s physiology, and how beautifully it works to bring oxygen to the bloodstream. When I was looking for a lab to join in Boston, there wasn’t anyone studying the lung in the way that I wanted to. Doug Melton was just getting interested in the pancreas, moving from his more basic work in the frog, and I thought that since the lung and the pancreas came from the same tube, I could help him figure out how to make a pancreas, and then once I was done with my training in his lab, I’d move a little anterior in the tube and make a lung. Doug thought it was a great idea, and we hit it off immediately. The best laid plans often change though. When induced pluripotent stem cells (iPSCs) came out, I immediately started working on making iPSCs into β-cells, and that got me hooked on stem cells. Doug really was a fantastic mentor to me, broadening my perspectives, encouraging me to explore the newest ideas and systems, while maintaining my fundamental interests.

What were your aims when you first started your lab?

As part of my interview at the Center for Regenerative Medicine at Massachusetts General Hospital I gave a chalk talk describing my ideas, but my actual research program ended up only very loosely related to that chalk talk. Essentially, I didn’t really know what I was going to do, except in very vague terms, and although I was empowered with new experimental tools and an understanding of the basis of experimentation, I was left with pretty much the same interests I had as a child: how do organs come to be?

When my lab started I was initially not so interested in the iPSC differentiation paradigms I had explored in Doug’s lab. Rather, I was enamoured of the developmental biology of the lung and particularly of tissue regeneration – it was so beautiful, and that’s what I focused on, primarily in the mouse because the biological tools were much more rigorous. Yet at the same time, I had always had this keen interest in human basic biology and in medicine – so I sort of put myself in Doug’s shoes again and eventually realised I would have to return to iPSCs. We reinvigorated that aspect of the lab, and then moved on to developing systems to grow adult lung stem cells, and I think we now have a way to use an actual human lung explant to do what I’ve always wanted to do: investigate the developmental biology of a regenerating human mini-organ.

What is it about the lung that is so fascinating as a developmental system?

For me one reason is evolution, which was another interest of mine since I was a kid. Back to my fishtank: it’s a mystery how fish came out of the water. How did the first lungs arise? The question of how it came about is fascinating. It’s also a fascinating organ in terms of its physiology, which I was introduced to during my medical studies. And something really appealing about the lung is that it is an organ par excellence for understanding regeneration. You have to breathe – which means there are physical forces and gas fluxes, and you inhale allergens, infections, toxins, dust and so on. All of the cardinal ways in which the environment could possibly perturb a tissue are captured in the lung. We’re really interested in how a perturbation in a tissue wrought by one of these injuries is resolved. I do often wonder whether there are some forms of simple equations that you could write to understand how cells come together to generate ensemble properties of tissues. It’s a very tall order, but there has got to be at least partially ordered ways of thinking about it, some ‘laws’ of regeneration.

And the lung is turning out to be a lot more complex than we previously thought. Doing single cell sequencing in collaboration with Aviv Regev we find two things: firstly, that there’s a whole new set of cells with interesting physiological properties; and secondly that many cells have specific immune signatures. The epithelium was considered to be pretty monotonous, and to interact with a panoply of weird different immune cells (you can see I’m not an immunologist!). Now it looks like there may be considerable crosstalk of particular epithelial cells types to particular types of immune cells. We also learned there are many subtypes of known cells, totally novel cells important for disease, and even new specialised structures in the airway.

For me it seemed like everything came together in the lung: aesthetics, evolution, developmental biology, the clinical aspect, and how the organ interacts with the environment. Somehow you just find your groove if you keep doing what you like, and the lung is a perfect lens with which to ask all the questions I’ve ever wanted to ask. I understand the developmental biology and some of the therapeutic issues, and then we are collaborating with people from other disciplines who look at the problem through different lenses. Some are computational biologists, physicists with new imaging techniques, immunologists, and I’m interested in collaborating with a whole suite of other biologists, including neuroscientists, evolutionary biologists and mathematical modellers. I don’t think that any single type of biologist is going to get close to sorting out the ‘laws’ of tissue behaviour unless they work together. It’s one of the most fun things in science: to head into totally new domains. It’s also a good way to make new friends! I get bored relatively easily and so like constantly hopping from one aspect of tissue biology to another: from evolution to signalling to force transduction to hypoxia. And I suspect many of these topics will become reincarnated in the lab as a new idea makes one of them seem appealing again. My tendency to dalliance works well for the postdocs as they can take their own projects with them to their own labs.

The lung is a perfect lens with which to ask all the questions I’ve ever wanted to ask

Many speakers at this meeting have emphasised the importance of developmental biology for stem cell research – how do you see the relationship between the two fields?

One way stem cell biology has helped developmental biology is that developmental biologists were so interested in embryogenesis, but tended not to think about the adult organism. Stem cell biology made developmental biologists think about the adult. The idea of stem cells is also just useful to convey enthusiasm, and that enthusiasm is incredibly important. Even in my own case, stem cells drew me into the problem of lung development and made me think about it differently. Stem cells are super interesting – but they are only one aspect of tissue function. There was a quote from Jean Rostand on the wall of Doug Melton’s lab which said ‘Theories come and go, but the frog remains’. I think this serves to remind us that there are tissues, organs and animals. Those exist, and all these different ways of doing science and naming cell functions are just different lenses with which to look at them, none of them complete in their own right and always evolving. As we analyse cells more deeply in the lung epithelium, we are finding that none is associated with a unitary specific function, but they all have many distinct activities. Stem cells aren’t just for replication and differentiation anymore. We have shown that they signal and I am sure they sense and perform necessary metabolic activities too.

It’s also important not to let the translational side eclipse everything else. The constant translational need, which is important, can become a drumbeat that moves you away from basic biology. Stem cells have this immediate connotation of being of use in a practical fashion, which is part of what makes them wonderful, but developmental biology has always come from the first principles of ‘I would just like to understand something’, and I think we cannot lose that because most of our truly game-changing ideas are a result of curiosity-driven basic biology.

Finally, stem cell research is an easy paradigm to communicate to patients and their families. It’s much harder to educate society at large about the interest and importance of basic biology. But I think we have to do that, especially with the modern political climate – there can’t be anything more important than to just explain to people why new knowledge is important.

So do you think the stem cell field as a whole is good at engaging with the public?

First of all, I think what the ISSCR does is spectacular, and I would in particular point out people like George Daley, Len Zon, David Scadden and Doug Melton, who are all great communicators, not just to the public at large, but also to our politicians. From my own perspective and background, I think we need to think about educating patients themselves, and to remember that they are vulnerable. I was a pulmonologist, and if I had a patient with, let’s say, idiopathic pulmonary fibrosis, they would walk into the office short of breath. More or less the one thing I could do for them was to give them oxygen – that is where the treatment ended. So you can imagine my inbox was full of messages from people who couldn’t get a new functioning lung with a lung transplant. These patients all wanted ‘stem cells’. I’m usually very straightforward in my response to them: although I empathise with what they have to go through because I’ve seen it first hand, I tell them that currently, there are no stem cell therapies for the lung, and that I would be very careful because there are a lot of people squirting stem cells into people without the proper science. But at the same time I try to give them the optimism of research, because I really do believe in it, and I am only getting more optimistic about it these days. I really think there is going to be a renaissance in terms of therapies produced through iPSCs and organoid models, and I try to fill patients with that kind of optimism. Unfortunately, you can’t give patients a timetable when they are desperate for a cure, but I can honestly say the research keeps moving faster and faster than I could ever have imagined.

At one point in my career, we wanted to make iPSCs from cystic fibrosis patients so that we could model the disease. I talked to some of my clinician friends, and they agreed to ask some of their patients to contribute, and the result was unbelievable – within a couple of weeks, we were turning people away. It’s overwhelming to see that kind of enthusiasm from patients: even if you tell them that this donation is very unlikely to help them personally, they still want to contribute. It’s also reciprocal: one of my graduate students working on a cystic fibrosis-related project asked me if he could meet a patient. My clinical friends again had a patient lined up to meet him before you could imagine it, and that patient spoke to my laboratory. It just blew them away and made their research so much more meaningful. If you are a PhD scientist working on a disease at the bench, you’re missing something. If you’ve come to meet patients, when an experiment fails 90 times, there is another reason you’re repeating it – it’s not just that you want the answer, but there’s a person that needs your help.

Being a PI was just intrinsically so much more fun for me than being a postdoc

When you started your lab, what was the transition to being a PI like?

I have to say that I found my post-doctoral fellowship very hard. I came from clinical medicine, and was used to knowing what to do – even if you couldn’t save every patient, you knew you could do your job well, and in a well-defined way. In science, it didn’t work that way and, quite frankly, it took me a long time to learn how to fail, and to appreciate the importance of problem solving. I am actually one of those scientists that did not love bench work, but rather I preferred to look at and think about data, talk to colleagues about new ideas and dream up future experiments. I had also always known, since I had been a Chief Resident in Internal Medicine at Massachusetts General Hospital, that mentoring was my absolute favourite thing. So it turned out that being a PI was just intrinsically so much more fun for me than being a postdoc, for almost every single reason. Again, I’d found my groove – being a laboratory head is the job I was ‘supposed’ to do, and it has only gotten better and better and better. I can’t imagine a job that is more fun, and I think the core value I treasure about it, perhaps even more than the study of living things, is the freedom it provides me.

One great thing about lung science for me is that, unlike haematopoiesis, neuroscience or immunology, it is so understudied, and so it’s just a great place for me to train young scientists. I’m so pleased to say that the first four scientists to have graduated from my lab all have their own labs. I don’t think I’ll ever have a very big lab – I like to put a lot of energy into every single postdoc and student, and hopefully have all of them leave and still love biology, no matter what they decide to do. That said, I’m hoping every single one will have their own laboratory if that is what appeals to them. Also, thanks to the Harvard Stem Cell Institute and my relationship to the undergraduate Stem Cell and Regenerative Biology Department at Harvard University, we always have undergraduates in the lab. It’s just fantastic to have people at every single stage of their education and career. Having undergraduates in my lab also means my postdocs and graduate students have a mentee, which is also a crucial part of growing as a scientist.

Do you have any advice for someone thinking about a scientific career today?

There have been editorials written by very prominent scientists that have said that we are training too many PhDs; I completely disagree with this! If anything I would say we are training too few, because while you can look at a PhD as a means towards one particular end, you could also look at it as a pure form of education that trains you to think and problem solve, and also to communicate in various ways. In some sense it’s a liberal arts education in terms of problem solving. But if you gravitated towards writing you could become a wonderful editor, if you gravitated towards ethics, politics or law, you can go into those roles. You can go into clinical medicine: a doctor who understands science is very valuable, just like a scientist who understands medicine. I remember in my own time, it used to be considered wrong to go to a company; now there are brilliant scientists who want to go to a company from day one. I love being a PI, and it’s a path I want to encourage but I’d like to see my young people in diverse spheres of human activity that captivate their imagination. The more people we have doing different things, but bound by a respect for scientific inquiry, the better.

I would say to a young person: explore and try to figure out what you love, and don’t worry too much about exactly where you’ll be 20 years from now because, in my view, no one really knows. Life is an experiment with no controls – at some level we’ll never know whether we’ve taken the right path – but if you’re constantly doing something you’re excited about, work becomes play. If you can live a life where work is always play, you have sealed the deal professionally. On top of that, you have to think about work-life balance – different paths have different constraints, and it’s important to talk to people who have lives that you’d want to emulate at work and also at home. Similarly with money: different people have different thresholds concerning what they are comfortable earning, and it’s important to think about this (but money can’t buy you love). I’d also advise people to ask for advice and help from people they respect. Mentors who listen and empathise, and who have a solid sense for your abilities can be invaluable for you as they were for me. I’ve found many people believed in my ultimate success and that was enormously encouraging at times when I did not think I could rise to the level of my own scientific bar.

Is there anything that Development readers would be surprised to find out about you?

Well the things I love outside of the lab are music, reading, the arts, travel and my family. I guess they’re all about exploration, and that might be the common theme. It’s a pretty interesting life to be able to combine all of these things.

Perhaps the other thing is that I’ve never left Boston since coming here for college, where in fact I met my wife in my freshman year! I’ve always found Boston to be supportive – people often think of it as an intense and competitive environment, but I’ve found it to be completely the opposite. Whenever I’ve asked for help, I’ve gotten it!

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The DORA Community Manager position is an opportunity for a Ph.D. or Master level fellow to gain experience in scholarly publishing and policy in Washington, DC. The DORA Community Manager will be headquartered at the American Society for Cell Biology (ASCB), which is located in Bethesda, MD. This position will report to the Director of Public Policy and is a 24 month assignment.

Essential Functions

Documenting best practices in Research Assessment

• Research and document examples of best practice of research assessment for different situations from a range of organizations, in particular DORA signatories.

Website and outreach

• Regularly blog, post to social media and send newsletters to signatories about issues related to DORA and progress on documenting best practices in order to keep awareness of this issue high.
• Work with relevant staff to maintain DORA website and ensure that it is a regularly updated source of valuable information.

Management of DORA signatories and data

• Assure the quality of the current data on institutional signers of DORA and recruit additional institutions, for example to improve the international coverage.
• Work with the Steering Committee to target and recruit new institutional subscribers.

Committee Management and Strategic Partnerships

• Develop strategic partnerships with groups who are working in highly related areas and can help to amplify the DORA message or assist in its activities.
• Provide monthly updates to the Core Steering Committee.
• Support the activity of the Core Steering Committee including creation of agendas for quarterly meetings.
• Liaise with individual members of the Core Steering Committee as necessary and with staff who are involved DORA nodes in other parts of the world.

Competencies

1. Technical Capability in communications, data management, and analytics
2. Strategic Thinking
3. Communication Proficiency
4. Project Management
5. Attention to detail
6. Creative problem-solving ability

Required Education and Experience

• Master’s or Ph.D. in the sciences preferred
• Knowledge of scholarly publishing, assessment of research
• Strong written and verbal communication including the use of social media

FOR IMMEDIATE CONSIDERATION:

Please e-mail your resume and one page cover letter with salary requirements to jobs@ascb.org

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