September 2017: Eutherian mammals (e.g. human, mouse) are often referred to as “placental” mammals, yet marsupials (e.g. kangaroo, koala) also have a fully functioning placenta, made out of the yolk sac. The evolutionary relationship between eutherian and marsupial placentas had not been investigated at the molecular level, so we performed RNA-Seq on a prized collection of wallaby placentas, provided by the Renfree lab! This was a collaboration with Michael Guernsey of Julie Baker’s lab (Stanford) and Marilyn Renfree (Univ of Melbourne). eLIFE | Commentary | Stanford press release
Here is an update on publications - these are 2015, 2016, 2017 and in press.
Buentjen, I., Drews, B., Frankenberg, S. R., Hildebrandt, T. B., Renfree, M. B. and Menzies, B. R. (2015) Characterisation of major histocompatibility complex class I genes at the fetal-maternal interface of marsupials. Immunogenetics67: 385-93 PMID:25957041
Cornelis, G., Vernochet, C., Carradec, Q., Souquere, S., Mulot, B., Catzeflis, F., Nilsson, M. A., Menzies, B. R., Renfree, M. B., Pierron, G., Zeller, U., Heidmann, O., Dupressoir, A. and Heidmann, T. (2015) Retroviral envelope gene captures and syncytin exaptation for placentation in marsupials. Proc Natl Acad Sci U S A112: E487-96 PMID:25605903
Fenelon, J. C., Shaw, G., Frankenberg, S. R., Murphy, B. D. and Renfree, M. B. (2017) Embryo arrest and reactivation: potential candidates controlling embryonic diapause in the tammar wallaby and mink1. Biol Reprod : PMID:28379301
Frankenberg, S. R., de Barros, F. R. O., Rossant, J. and Renfree, M. B. (2016) The mammalian blastocyst. Wiley Interdiscip Rev Dev Biol5: 210-32 PMID:26799266
Gamat, M., Chew, K. Y., Shaw, G. and Renfree, M. B. (2015) FOXA1 and SOX9 Expression in the Developing Urogenital Sinus of the Tammar Wallaby (Macropus eugenii). Sex Dev9: 216-28 PMID:26406875
Hetz, J. A., Menzies, B. R., Shaw, G., Rao, A., Clarke, I. J. and Renfree, M. B. (2015) Growth axis maturation is linked to nutrition, growth and developmental rate. Mol Cell Endocrinol411: 38-48 PMID:25896544
Hetz, J. A., Menzies, B. R., Shaw, G., Stefanidis, A., Cowley, M. A. and Renfree, M. B. (2016) Effects of nutritional manipulation on body composition in the developing marsupial, Macropus eugenii. Mol Cell Endocrinol428: 148-60 PMID:27032712
Laird, M. K., Hearn, C. M., Shaw, G. and Renfree, M. B. (2016) Uterine morphology during diapause and early pregnancy in the tammar wallaby (Macropus eugenii). J Anat229: 459-72 PMID:27168485
Martin, F. C., Ang, C. S., Gardner, D. K., Renfree, M. B. and Shaw, G. (2016) Uterine flushing proteome of the tammar wallaby after reactivation from diapause. Reproduction152: 491-505 PMID:27486272
Pharo, E. A., Renfree, M. B. and Cane, K. N. (2016) Mammary cell-activating factor regulates the hormone-independent transcription of the early lactation protein (ELP) gene in a marsupial. Mol Cell Endocrinol436: 169-82 PMID:27452799
Renfree, M. B. (2015) Embryonic Diapause and Maternal Recognition of Pregnancy in Diapausing Mammals. Adv Anat Embryol Cell Biol216: 239-52 PMID:26450502
Renfree, M. B. and Schmid, M. (2015) Evolution of external genital development. Preface. Sex Dev9: 5 PMID:25591793
Saragusty, J., Diecke, S., Drukker, M., Durrant, B., Friedrich Ben-Nun, I., Galli, C., Göritz, F., Hayashi, K., Hermes, R., Holtze, S., Johnson, S., Lazzari, G., Loi, P., Loring, J. F., Okita, K., Renfree, M. B., Seet, S., Voracek, T., Stejskal, J., Ryder, O. A. and Hildebrandt, T. B. (2016) Rewinding the process of mammalian extinction. Zoo Biol35: 280-92 PMID:27142508
Stickels, R., Clark, K., Heider, T. N., Mattiske, D. M., Renfree, M. B. and Pask, A. J. (2015) DAX1/NR0B1 was expressed during mammalian gonadal development and gametogenesis before it was recruited to the eutherian X chromosome. Biol Reprod92: 22 PMID:25395677
Excel is commonly used for processing data, but the default graphs lack a little something when it comes to scientific presentation (I guess perceptual accuracy and aesthetics are not a key criterion for business graphs, so we get stuck with ugly graphs).
I made these notes a few years ago using Office 2007, but the process is similar with the more recent versions.
Plotting graphs using Excel / Office 2007
First get your data: here is some I prepared earlier… we want to plot the data in the column under “y” with the sem under SEM
In this example I will do a column chart so I have put in a set of labels for the groups I am potting – this makes it easier to get the labels on the x-axis quickly. I have selected the labels and y data
Change to the “insert” Tab and select the graph type – here I will select a column plot
… and choose a simple plot type suitable for the data.
The graph is created with labels but no error bars:
I have made the plot narrower by dragging the right border to the left.
I have selected the data by clicking on one of the columns
Under the “Layout” tab choose “analysis and select “error Bars” and under this choose “more error bars”
Use the menu under “Error amount” to pick “custom”, and select the error data for both the plus error bar and the minus error bar. Your graph now should have error bars showing the SEM.
Now you can tidy up the graph (Layout tab), add axis labels (Layout::Axis titles) and graph title (Layout::Chart Title), remove or adjust the legend (Layout::Legend), format the axis numbering (Layout::Axis), add lines of best fit (Layout::Trendline), format/remove gridlines, etc etc.
With a bit of fiddling you can make the graph look reasonably good
You can even add adornments like markers to indicate statistical significances etc… (Insert::Shapes gives you lines, boxes etc - the usual suite of office drawing tools)
Alternatively you can paste the graph into PowerPoint -- if you use Paste::Special::Enhanced Metafile, then ungroup (twice) you convert the image of the graph into Microsoft office drawing objects which you can edit using the normal PowerPoint tools. I often find this approach makes it easier to set line thicknesses, colours, text fonts etc etc than doing the equivalent in Excel graph mode. It also allows you to lay out multiple graphs or other images onto a page, with easy resizing etc to make things fit together aesthetically. Of course doing it that way loses the ability for automatic recalculation of the graph in the presentation by changing the data etc that you get if you paste the graph in as an excel object (the default paste mode). Have a play and you can choose which ever approach is most appropriate for your needs.
Once you have a graph format in excel you can re-use it with different data too. Just copy and paste into a new location to get a duplicate, then under the Design tab choose Select data to feed in the new numbers. The formats, colours, fonts etc that you so laboriously chose will stay with the graph, so once you have changed the data used by the graph you should have a nice graph of your other data (you will probably need to change the graph axis ranges if you set these manually; you may need to redraw any adornments you added - P values, text, lines etc, and perhaps move the legend to a new location).
Australia is home to a unique spectrum of mammalian species. Our animals are our national heritage. Our Coat of Arms is held up by two of them – significantly, both are animals that cannot walk backwards! Internationally we are renowned for our iconic mammals, in particular the kangaroo, koala and platypus. Yet sadly, research on our native species is not a national priority. Marilyn Renfree has spent most of her research life uncovering the secrets of marsupial reproduction and development, comparing them to eutherian mammals, and discovered the many novel ways that make marsupials ideal biomedical models but also how to enhance their management and conservation. What would Australia be without our monotremes and marsupials? This conference is to acknowledge the contribution of Marilyn Renfree to Australian science in the fields of reproduction and development in the year of her 70th birthday.
This conference will cover a diverse set of topics in the field of reproduction and development, divided into five main themes over two and a half days. These themes are: Contraception and Conservation; Development and Diapause; Sex and Reproduction, Genomic Imprinting and Marsupial and Monotreme Genomics
About one in ten males (including myself) have some degree of red-green colour blindness (and 0.5% of females), so you should bear that in mind when you are making graphics to display to others. Red-green colour blindness comes in varying degrees - it isn't necessarily that red and green cannot be distinguished, but that are clearly different to people with normal colour vision may be hard (or impossible) to discriminate for others. Here are some hints.
Thin lines are difficult. Thin lines don't activate many colour sensors (cones) in the retina, so it may be difficult to discriminate the colours of thin lines. On the graph to the right I am hard pressed to tell which line is which colour. In fact, I am not certain these lines are coloured - they could be grey.
Making the lines thicker aids clarity and makes the colour MUCH easier to discriminate. In this version of the graph I can work out which line is red and which is green without too much effort, at least at the size this shows on screen in this page. If one is sitting at the back of a theatre looking at a tiny version on a screen ... then we are back to the too few cones activated problem.
Using solid symbols adds extra colour area to activate more cones. This helps. But for those on the weaker end of the colour blindness spectrum it still makes for difficulties.
Here we have a secondary means of discriminating the lines - hollow vs solid symbol and dashed vs continuous line. Now the lines are clearly distinct, even for those who are totally colour blind.
Alternatively, to accommodate red-green colour-blindness, use a colour palette which is friendly. The blue line is clearly distinct from the red ??? or is it green or grey??? Whatever, the two lines are distinctly different because I can see blue well.
Another approach is to use different degrees of brightness, as well as different colours, for the lines and symbols. One can see differences in shade clearly on this version even if you cannot discriminate any colours.
There is a lot of information on the web that explains the issues of colour blindness, and many tools to help you select colours (and other strategies) that will make your data presentation much clearer for colour blind people (and, in reality, also easier for people with normal colour vision). Here is a selection of links that look useful ... or do your own search (eg https://duckduckgo.com/?q=colourblind+pallette&t=newext&atb=v346-1&ia=web).
Sometimes one may want to generate a PowerPoint slide with a set of images - for example a set of micrograph images to discuss with your colleagues (or a lovely set of your holiday photos to make your friends jealous). You can do this by manually adding each image, one by one, then resizing, repositioning, formatting .... , but there is a much quicker way. Here is a guide to automating the process.
If you are using fluorescence microscopy you may need to merge images taken with different filter sets - for example DAPI to pick out nuclei together with fluorescent staining with or or more specific antibodies. Commonly you will want to merge these images into a composite. Image optimisation and merging can be achieved easily using the free Fiji package with ImageJ.
If you don't have the program, you can get it from http://imagej.net/Fiji/Downloads. More information on Fiji at http://imagej.net/Fiji. ImageJ and Fiji come as a portable application - ie you do not need to install, just unpack the download and run the program from wherever you downloaded it.
The following notes were done using the Windows version; on a Mac there will be cosmetic changes in the appearance of the application and dialogues, but the processes should be essentially the same.
In the folder where you unpacked Fiji you will find the ImageJ application. Run this program:
The ImageJ application opens. On windows it looks like this.
Use the menu File::Open (or use the shortcud Ctrl-O) and locate and open the first image file. Repeat to open the second image file. Let us assume you have opened two files called red.jpg and blue.jpg. By default these will open as separate floating windows. Note that you could use 3 files and merge red, blue and green channels just as easily.
For convenience I have maximised the ImageJ main window to fill the screen and used the Window::Tile menu to organise the two images.
Select one of the images and use the Image::Adjust::Brightnes/Contrast menu to adjust the brightness, contrast etc. Have a play with the settings to get a feel for how they work.
In this case the red channel seems a little dull.
I have reduced the maximum slider to brighten the image (don't take it too far or you will lose any graduation in brightness in the brighter areas) and I have moved the minimum slider a little to remove some of the background (again take care that you do not remove any meaningful information in this process. If in doubt, don't adjust).. If you adjust, make a note of the settings you have selected (in this case Min=19, max=148). These changes are equivalent to changes you could achieve by altering the camera settings when you took the photo. The key is to avoid changes that alter the significant data in the image, rather than shifting the brightness levels. Repeat the process of adjusting on the other channel. Save the adjusted images (Menu FIle::SaveAs...) - I suggest using a new name like red-adjusted.jpg and blue-adjusted.jpg.
Now you want to combine the images. To do this you first need to make them both grayscale images. Use menu Image::Type::8-bit for each file.
Now merge the images with the Image::Color::MergeChannels...
In the dialogue, select which image you want for which colour channel and click OK.
You now have a merged image. If it looks OK, use File::SaveAs to save the merged image with a suitable name.
If you think you can get a better image with different adjustments to the colour channels, you can go back to your original files, re-adjust (I recommend starting from the very original files, re-adjust the brightness and contrast bearing in mind the settings you used before, and re-merge. If you readjust the adjusted files you will likely lose more image quality in the process.
Note that ImageJ also has filters for image sharpening (Process::Sharpen or a more customisable sharpening via Process::Filters::UnsharpMask), but note that these can be overdone, and they cannot be undone using Ctrl-Z or Edit::Undo, so save your image before you try them so you dislike the result you can close the mangled image and re-open the saved version. If you have photoshop or GIMP you can also use the unsharp mask process I describe in a previous post.
If you are using fluorescence microscopy you may need to merge images taken with different filter sets - for example DAPI to pick out nuclei together with fluorescent staining with or or more specific antibodies. Commonly you will want to merge these images into a composite. Image optimisation and merging can be achieved easily using the free Fiji package with ImageJ.
September 2017: Eutherian mammals (e.g. human, mouse) are often referred to as “placental” mammals, yet marsupials (e.g. kangaroo, koala) also have a fully functioning placenta, made out of the yolk sac. The evolutionary relationship between eutherian and marsupial placentas had not been investigated at the molecular level, so we performed RNA-Seq on a prized collection of wallaby placentas, provided by the Renfree lab! This was a collaboration with Michael Guernsey of Julie Baker’s lab (Stanford) and Marilyn Renfree (Univ of Melbourne).
