I’m dabbling a little with PlantCV, an open-source image analysis package with lots of pre-built features for doing plant image analysis. It can be installed using the Conda package manager system, which means I won’t have to mess around with all the dependencies. It can also be installed on a RasPi, which is also very interesting. It isn’t clear to me how well it works on roots, as most of the examples I recall seeing seem to focus on leaves and stems. I can’t think of why this could be a problem, but I’d like to poke around a little bit with some example root files once I capture them using the RasPi hq-cam next week.
I dabbled a little today with the RPi high quality camera using the same lens I use for the other image capture systems (a 75 mm fixed C-mount lens) with a 70 mm extension tube. I took a photo of a ruler and measured the distance of 3 mm in pixels, which I found to be 2144 pixels. The vertical distance in the frame was around 4 mm, which seems to be in the neighborhood of what I want for an Arabidopsis root experiment. This may mean I don’t really need a panel of multiple LEDs to achieve even background illumination, at least when using this lens and tube setup for a single root tracking experiment. OG ROTATO scaling is 664 µm per 100 pixels, or 6.64 µm/px. The RPi HQ camera has a resolution of 3000 µm in 2144 pixels, or 1.4 µm/px. This is 4.75x that of the old system.
If I’m going to use it for regular root experiments, I might remove the IR filter that’s glued to the sensor.
I’ve started to dip my toe in the pool of bioinformatics methods, using our recent data sets as an incentive to learn what I’m doing. In reading more about using Salmon, it seems I should build a “decoy-aware” index. So the commands below source accomplish that.
grep "^>" <(gunzip -c Arabidopsis_thaliana.TAIR10.dna.toplevel.fa.gz) | cut -d " " -f 1 > decoys.txt
sed -i.bak -e 's/>//g' decoys.txt
cat Arabidopsis_thaliana.TAIR10.cdna.all.fa.gz Arabidopsis_thaliana.TAIR10.dna.toplevel.fa.gz > gentrome.fa.gz
salmon index -t gentrome.fa.gz -d decoys.txt -p 12 -i salmon_index --gencode
This pegs the CPU at 100% but does not fill the 8 GB of RAM on this machine. This took about 7 min on this machine.
After this I will do another trial run with a few reads and compare the mapping rate with this decoy-aware index compared to the raw transcriptome I used yesterday (I renamed the output from yesterday quants_1).
New work out of Wolfgang Busch’s lab in Vienna, led by Elke Barbez, describes a new and relatively simple method for measuring the pH of plant cell walls:
Here, we present a fluorescent dye that allows for the correlation of cell size and apoplastic pH at a cellular resolution in Arabidopsis thaliana.
I’m really pulling for the SpaceX launch scheduled for this week, as my colleague and friend John Kiss’s experiment is launching on it:
Our experiment uses the same hardware and facilities on flight as John’s, so I’m also really hoping everything goes well up there, otherwise we’ll have problems getting approved to launch.
UPDATE 2017-06-01: Today’s launch was scrubbed due to lightning in the area; they’re going to try again on Saturday.
I’m looking forward to the day that my seeds are transported to the ISS on one of these babies! There’s still a long way to go before my project is even ready to apply for a flight position, but I’ve started working with support scientists to schedule all the tests that need to be done. It’s going to be a very busy summer around my lab!
Read more about today’s launch, known as the CRS-6, at NASA’s page about the mission.
Whenever I teach on seeds, either in my non-majors Food class or my Plant Physiology class for majors, I can’t help describing them as the children of the mother plant. I know, not exactly creative, but it helps to paint a picture of the roles of the parent plant and the seed. I like to talk about how the endosperm or other food reserve is like a packed lunch, put there by the caring mother to feed the baby plant as it germinates and becomes able to feed itself. And what kind of parent sends its babies out without a coat? It usually gets a few chuckles, at least, to put this all in human terms.
That coat on the seed? Sometimes it’s a jacket, and other times it’s more like a down coat, and the mother plant chooses based on the temperature. I’m not making this up. In a study published this week, plant scientists link the toughness/thickness of the seed coat to the temperature endured by the mother plant. If the mother experienced warmer temperatures, it will make more of a protein that limits the production of tannins in the fruit. Less tannin makes for a thinner seed coat and faster germination. On the other hand lower temperatures cause the mother plant to make more tannins, leading to a thicker coat. Simple, yet remarkable.
For the last several months I’ve been working on a manuscript to be included in an edited volume tentatively called Plant Gravitropism: Methods and Protocols. It is part of a series called Methods in Molecular Biology, published by Springer.
My contribution focuses on ROTATO, the image analysis and feedback system we use routinely in my lab to measure root gravity responses. The objective of the series is to allow “a competent scientist who is unfamiliar with the method to carry out the technique successfully at the first attempt,” which seems pretty unlikely to me. I can’t think of a single experiment that I’ve every carried out successfully on the first try, but that’s another matter. I’ve been surprised by how hard it’s been to write this, so I thought I’d do some thinking out loud to try to gain a little insight into my struggle.
I think some of my struggle has come from being too close to the method to see it with “beginner’s eyes.” I’ve been working with ROTATO since it was a pile of parts stripped from IBM PCs (we used the computer power supply for 5 V DC and the stepper motor from the floppy drive). I watched over my friend Jack’s shoulder as he wrote the software to make it work. I know the ins and outs of how it works and what makes for a good experiment. Through the years I’ve had a tough time teaching my students how to get good data with it, and I think that’s in part due to the hidden assumptions I make about it. Dragging those assumptions out into the light has been an ongoing process, and writing this paper has been helpful.
Another aspect of the struggle is with how to handle the software part of the method. I am not releasing the code (it’s not mine), and even if I could it wouldn’t do much good because of its dependence on an obsolete frame grabber card. So I’m trying to include enough detail about how it works to allow a scientist/programmer to reimplement the method. But I’m a biologist, not an engineer, so I’m struggling with how much to say and how to say it. I think this is the heart of the issue, that I’m trying to bridge the worlds of biology and engineering.
This is, in fact, what ROTATO is about, and what makes it so important. It takes pictures of a biological response and uses them to control the position of the organ doing the response. It is clever, naive in certain ways, clunky, finicky, crashy, and it works. It has allowed us to learn new things about how roots respond to gravity. So that’s what I’m trying to convey in this methods paper, how to make a ROTATO that works well enough to learn new things, of which there are plenty, I am sure.