Science Fun – Science faire in Ukraine

A science faire was held on May 22nd in Ternopol – IV at Theater Square.  The goal of the event was to popularize science among young people and excite the next generation of scientists in Ukraine.

There were about a dozen tents where schools and universities demonstrated scientific experiments in chemistry, biology and physics to the public.  Students of the Faculty of Chemical and Biological (http://chem-bio.com.ua), part of Ternopil Volodymyr Hnatyuk National Pedagogical University, http://tnpu.edu.ua)  presented PhotosynQ, created by scientists at Michigan State University.

Schoolchildren, students, and young scientists had the opportunity to personally touch science in the truest sense of the word. With the device MultispeQ, anyone could measure the progress of biophysical processes otherwise invisible to the eye in leaves of Phaseolus vulgaris plants and share data throughout the world via the PhotosynQ platform.

The interest and excitement generated at this event shows that science can be very interesting and exciting thing that unites the world.

by Andriy and Nataliia Herts

MultispeQ in Malawi

As the development team focuses on manufacturing the MultispeQ v1.0, we’ll have a series of articles from project partners and developers we hope you find interesting. We’ve had a great response to the pre-release, so thanks to everyone! – Greg

malawi multispeq users
(left) Masters student Hellen Mwale and Kareem Longwe practice data collection with the MultispeQ to get ready for data collection on FRN’s in Central Malawi. (right) Frank Mnthambala and Margaret Chiipanthenga collect data on soybeans in a greenhouse in Bvumbwe, Malawi to identify drought tolerant cultivars.

Over the past year we have partnered with a number of researchers in Malawi who have collected over 30,000 measurements on 15 different projects using PhotosynQ. Our partners in Malawi include researchers from the Department of Agricultural Research Services (DARS), Lilongwe University of Agricultural Research Services (LUANAR), and a private seed company (Global Seeds). I just got back from 2 weeks in Malawi meeting with them and getting their feedback on PhotosynQ.

Access to high quality laboratory equipment is lacking in Malawi, so researchers are very excited about what information MultispeQ can provide to them. In many cases, field based plant breeding and cropping systems research has been limited to data that can be recorded with a scale and tape measure. With PhotosynQ, they can see beyond what happened (e.g. how the crop yielded) and can start to understand the reasons why crops performed the way they did (e.g. how plants regulated photosynthesis to adapt to their conditions).

Despite a lot of enthusiasm, there are some real challenges that need to be overcome to collect quality data. Internet infrastructure in Malawi is very poor and the internet is often too slow to work effectively on-line or doesn’t work at all. This makes it difficult for users to create projects and analyze results. But it also means that users don’t update their mobile app very often. So they may still be trying to work around bugs in an older version of the app that we have already fixed in a newer release.

Another challenge to using PhotosynQ in Malawi is frequent ‘brown-outs.’ Partners can’t count on the electricity being on when they need to recharge their phones or MultispeQ batteries. Some partners in Malawi have responded by using ‘power banks.’ A power bank is a small extra battery that can hold enough charge to recharge your phone 2-3x. They will plug it right into their phone or tablet’s usb port in the field and recharge their mobile device while taking measurements. It’s one more thing to hold onto in the field, but it solves a problem.

malawi battery pack
A student uses a power bank to keep his mobile device working in the field.

This coming year our partners have even more interesting projects planned. Everything from variety trials of soybean, sweet potato, maize, common beans, and pigeonpea to studies analyzing the effects of cropping systems on crop performance (click here to see a list of existing projects + data). These projects will take place on research stations and smallholder farms all around Malawi.

Two Master’s students from LUANAR will be using PhotosynQ on Farm Research Networks (FRN’s) to assess how different legume-based cropping systems can increase production on smallholder farms. FRN’s are research trials that are located on smallholder farms, instead of research stations, and are managed by the farmers themselves. As such, they paint a much more accurate picture of how ‘new’ cropping systems affect crop production on smallholder farmers. What’s really exciting is that these 2 students will be collecting data on FRN’s that include over 300 farms in 3 districts in Malawi. Even if they only collect PhotosynQ data on 1/3 of the farms, it will be the largest on-farm data collection using PhotosynQ to date! And it will take place with poor internet connectivity and frequent power outages!

More Malawi Projects

Dan TerAvest

Who’s using the MultispeQ? Let’s see…

As we approach the next version of the MultispeQ, I wanted to share stories from a few of our beta testers – Matt, Karen, and Kay from the MSU Kellogg Biological Station, Jeremy Harbinson from the University of Wageningen, and Jesse Traub from Michigan State University.  You can find even more stories here.  Hope this sparks some interesting ideas for applications in your lab, home, farm, work, or play 🙂

Kellogg Biological Station

Matthew Carey (REU student), Karen Stahlheber (postdoc) and Kay Gross (KBS director), Ecologists

Matthew Carey, measuring grass at Kellogg Biological Station
Matthew Carey, measuring grass at Kellogg Biological Station

Our group is interested in the response of switchgrass (Panicum virgatum) varieties to drought, and how that interacts with fertilizer use. We installed rain reduction shelters on fields planted with switchgrass ~6 years ago and managed either with or without fertilizer. These shelters reduce available soil moisture and simulate drought conditions that might occur with future global change. Throughout the summer, we monitored plant growth, chlorophyll fluorescence, xylem tension, and the abundance/diversity of mycorrhizae (fungi that live in symbiosis with plant roots). The eventual goal (after several field seasons) is to understand how the interactions between fertilizer application and arbuscular mycorrhizae diversity affect ecosystem services such as productivity, pathogen tolerance, drought tolerance, and soil carbon storage.PhotosynQ has been a great asset to our project because it allows us to measure any stress the plants may experience due to high light/low water conditions throughout the summer. By measuring Fv/Fm in the early hours of the morning we can understand if plants underneath the shelters have suffered damage to their photosynthetic machinery compared to control plants receiving ambient rainfall. We also can use the devices during the day to assess general photosynthetic performance and see if that differs between varieties or is changed by fertilizer use.

If PhotosynQ is successful, it could allow farmers of cellulosic biofuel crops like switchgrass to use the same tools to monitor their plants for stress or for responses to fertilizer.

Plant Sciences Department, University of Wageningen

Jeremy Harbinson, Plant Physiologist / University Lecturer

Jeremy Harbinson
Jeremy Harbinson

We try to understand better the operation, regulation and limitation of photosynthesis in vivo, both from physiological and genetic perspectives. We plan to use the PhotosynQ in teaching and as a tool for the more or less routine monitoring of leaf-level photosynthesis of plants in the field. The PhotosynQ concept opens many doors. In terms of eco or environmental physiology – or phenotyping, particularly that of photosynthesis, it enables large scale data collection in a way that has previously not been possible. It helps close the gap between the diversity for physiological responses encountered in the field due to environmental and genetic reasons and the time required to get good data relating to these responses. Low-cost, fast, and measuring a large number of processes makes many things possible that cannot be done with existing instruments which are expensive, often slow and limited in what they can measure. It is a revolutionary concept.

Department of Horticulture, Michigan State University

Jesse Traub, PhD candidate

Jesse Traub, MSU Horticulture
Jesse Traub, MSU Horticulture

We are investigating physiological differences among contrasting dry bean genotypes in their response to drought and heat stress. We are especially focusing on the response of photosynthetic parameters to these stresses. The PhotosynQ platform enabled us to screen large amounts of germplasm to determine at what severity of stress different bean genotypes started to become damaged.  If PhotosynQ became a standard tool for my discipline of plant physiology and plant breeding, it would provide an easy way to compare otherwise unrelated experiments and sets of data. This would be great for the reproducibility of experiments! I admire that the PhotosynQ project has been committed to making their hardware, software, and data freely accessible to all to use, learn from, and modify. I hope such sentiments continue to grow in the academic world.

This is just a few of the 100 or so people who used the MultispeQ Beta

You can find a few more stories about using PhotosynQ to develop tools for corals, identify agricultural best practices in Malawi, optimizing light in greenhouses in the Ukraine, and detect disease in soybeans in Michigan.  Next time, a progress report on the MultispeQ v1.0!

Hello Fargo, I’ve come for your beans!

Project: Bean Variety Trials at North Dakota State University
Project Leads: Juan Osorno and Ali Soltani, North Dakota State University
Goal: Collect photosynthesis and plant health data on 150 varieties of common bean for eventual QTL (genetic) mapping.

Project Page
View and analyze the data (create a login if necessary)
Juan Osorno’s NDSU page

 

Hello Fargo!
Hello Fargo!

This week I went to Fargo, North Dakota to meet with Professor Juan Osorno and post-doc Ali Soltani, bean breeders at North Dakota State University. I bet you didn’t know that NDSU has one of the premier bean breeding programs in the US – well they do!

On my flight in, I told the guy next to me I’d never been to North Dakota before, and his response was “You’re going to love it”… Love it? North Dakota? Well, yes, I did love it. People were nice, and it appeared that everyone was there because they wanted to be, which makes sense, you don’t end up in North Dakota for no reason. Agriculture is booming, and the the fields are gigantic (at least in comparison to the ones I was used to growing up in central New York). So, what were we doing there? I’ll let Ali give a recap:

So our goal is to show that you can correlate photosynthetic outcomes to actual genes or groups of genes.   This has so far proven difficult and slow to achieve for breeders especially in comparison with the dizzying pace of mapping the genome, which has been automated and has come down in cost many orders of magnitude over the last 15 years. We took measurements of 150 different varieties with 6 replicates each (900 measurements total).  Each measurement included two protocols: SPAD (a measure of leaf greenness which correlates to Nitrogen content) and Phi2 (a measure of photosynthetic efficiency).

Stephan collecting data using the Android app
Stephan collecting data using the Android app

It took us some time to get ready to collect data.  We had to go to a coffee shop to get internet to make sure everyone had an account at PhotosynQ.org and their cell phones had the PhotosynQ android app installed correctly.  But once we got to the field (a full 1.5 hours away!), taking measurements was a snap.  The only technical problems we had were swapping batteries as they needed to be recharged – that was a big success for us, and shows we’re ready to do real work with this thing!

MultispeQs charging their batteries after a hard days work.
MultispeQs charging their batteries after a hard days work.

So let’s look at some preliminary results using the online analysis tool (so you can view and play with the data too!  Note that you may have to create a login first). This tool is intended to be a Swiss Army knife of sorts – it can do lots of quick analysis, but none of them too deeply.  If you need to do multiple regression analysis… you’ll probably have to just download the data 🙂  We might to see more data in this project this week, as Ali and Stephan go back to a second field, we’ll see.  Also, Ali is working on more in depth device comparisons, to try to use statistics to parse out the variation coming from the device versus that coming from the varieties themselves.

 

We can also compare two variables on the X and Y axis. Here we have LEF (linear electron flow) a measure of energy from photosynthesis compared to light intensity. Each device has a separate series. These differences may be due to calibration, or differences in plants, hard to know yet.
We can also compare two variables on the X and Y axis. Here we have fluorescence in the steady state (normal light) versus that from a saturated state (very high light).  These differences may be due to calibration, or differences in plants, hard to know yet.
SPAD (a measure of greenness) was fairly consistent across devices as you can see. Some variation is due to the fact that each device only measured 60 of the 150 varieties, so there's not perfect overlap there.
SPAD (a measure of greenness) was fairly consistent across devices as you can see. Some variation is due to the fact that each device only measured 60 of the 150 varieties, so there’s not perfect overlap there.
The most important outcome from this trial was to determine if 6 devices could produce consistent results. As you can see here, device 43 was reading too high on light intensity PAR - we'll have to investigate that!
The most important outcome from this trial was to determine if 6 devices could produce consistent results. As you can see here, device 43 was reading too high on light intensity PAR – we’ll have to investigate that!
This is a simple average of Phi2 for 15 varieties. The black bars are 1 standard deviation.
This is a simple average of Phi2 for 15 varieties. The black bars are 1 standard deviation.  Anything statistically significant here?… mmm… not quite yet.
Histogram showing Phi2 (photosynthetic efficiency) for the entire sample - distribution isn't too bad!
Histogram showing Phi2 (photosynthetic efficiency) for the entire sample – distribution isn’t too bad!  Not a lot of outliers which means the MultispeQs worked ok.
temperature by time
This graph doesn’t show much from a plant health perspective, but it does show how temperature in the device varied over time. In general we’ve found that people’s hands heat up the device the longer they hold it. You can see that effect here for each device (each series snakes upwards), and you can see how long it took us to take all our measurements. This is something else we need to address in the next version.
Here's a map of the field colored by device ID. The entire field is offset to the left by about 10 meters. However, you can see that each user measured from left to right over only a few rows, which was correct - cool!
Here’s a map of the field colored by device ID. The entire field is offset to the left by about 10 meters. However, you can see that each user measured from left to right over only a few rows, which was correct – cool!

 

 

Attaching sensors to MultispeQ part 1: YwRobot soil moisture sensor

So now that production is in process (moving along, we had a minor hardware patch to apply so we’re waiting for a new shipment of boards, but we’re all ready to crank out boards otherwise) and we feel good about the core measurements of the MultispeQ, we decided it was time to see if we could easily slap on other sensors into the PhotosynQ framework.  First up – the YwRobot’s Soil Moisture sensor (actually, soil conductivity, but we’ll get into that later.  Let’s start by talking about how to connect to MultispeQ and what tools can be connected —

What can connect to MultispeQ?

The MultispeQ is built around a Teensy 3.1, so please see the Teensy’s capabilities here for details.  The lights, detectors, and existing sensors already use most of the existing pins (see here for Teensy pinout), but there are a few extra available pins:

  • 2 digital pins and 1 analog pin through-hole pins.
  • 10+ analog pads which are not through hole
  • I2C line
  • DAC
  • 3.3V line and ground

So the simplest device to connect is anything which outputs a 0 – 5V signal.  Our YwRoboto sensor does just that!

Soil Moisture Background and Experiment

Measuring soil moisture can be approximated by measuring soil conductivity.  Conductivity is influenced by the movement of ions in the material between the electrodes – see image below for what the device looks like.  So for a given soil type, more water increases the movement of ions.  However, it is very difficult to compare different soil types, because they will have different concentrations of ions and therefore different results.  So this is useful for relative soil moisture changes in a single location (like your house plant), not in different locations (like different fields in different soil types).

In our quick and dirty test, we made a matrix of 4 soil types x 3 moisture levels (from high to none) which you can see in the image below. We connected the pins between the moisture sensor and the MultispeQ as follows:

through-hole locations to connect to the MultispeQ
through-hole locations to connect to the MultispeQ
YwRobot Pin MultispeQ (Teensy 3.1) Pin
GND GND
VCC 3.3V
OUT A14

 

The YwRobot Moisture Sensor attached to a MultispeQ in potting soil
The YwRobot Moisture Sensor attached to a MultispeQ in potting soil
4 types of soil (3 potting mixes and 1 clay dense dirt from outside) with 3 levels of moisture (no moisture, 6ml moisture, and completely drenched)
4 types of soil (3 potting mixes and 1 clay dense dirt from outside) with 3 levels of moisture (no moisture, 6ml moisture, and completely drenched)

The communication protocol between the PhotosynQ chrome app (or Android app) and the MultispeQ is in a JSON format.  In order to request the information from pin A14 (also referred to as pin 40), just add it to the JSON.  Below is an example simple JSON which requests temperature, relative humidity, and the analog read from pin 40.  It also specifies to take 2t00 measurements with a 2 second delay between them:

[{“environmental”:[[“temperature”,0],[“relative_humidity”,0],[“analog_read”,0,40]],”measurements”:100,”measurements_delay”:2}]

You can also create this protocol using menu-based drag-and-drop tools through the Chrome app, but I thought I’d give the details here so you could see it.

Initially, I compared soil moisture in each of 12 samples above.  Here’s what I got:

Measured soil conductivity of the 12 different soil samples. The two lowest moisture levels are not that different and sometimes opposite of expected
Measured soil conductivity of the 12 different soil samples. The two lowest moisture levels are not that different and sometimes opposite of expected

As you can see, the 6.5ml water addition versus dry doesn’t show a consistent positive correlate, which doesn’t make sense.  I think it may be due to the fact that I had to take out and put back in the probe each time.  So I just tried placing the probe in soil, and adding moisture to the surface without affecting the probe.  These results were much closer to what I would have expected:

Kept the probe in a single soil mix, and sprayed water onto surface to simulate rain. This makes much more sense, though even wiggling the probe slightly significantly changes the signal.
Kept the probe in a single soil mix, and sprayed water onto surface to simulate rain. This makes much more sense, though even wiggling the probe slightly significantly changes the signal.

Conclusions

Overall, this sensor definitely relates to soil moisture, and the completely saturated cases of different soil types even show similar absolute response (about 45k counts). However, at less than saturated levels, soil conductivity varies quite a bit between the different soil types at least from this quick little introductory test so probably soil moisture can be accurately measured at a single location.

In terms of integrating this sensor into PhotosynQ, it was pretty easy. Connect 3 pins, add one small line to tell the device to look for it, and vioala – graphs!

Next Steps

The next step is to actually stick this thing into my yard and see what happens. We’ve been talking about trying to pull in weather data into PhotosynQ so you can correlate and analyze that in addition to the sensor data, which would be particularly fun here. Also, I think there is a new version which is gold plated and therefore much more robust which I’ve already ordered to play with. Finally, I should create a real research project (which others can join and participate in) out of this, instead of just taking one off measurements. Then we can see how the PhotosynQ online analysis tool could be useful to analyze the resulting data (see here for example of recent data taken in bean fields in North Dakota – please be patient while the data loads!).

PhotosynQ out in the world

Beta MultispeQ device with Android App on tablet
Beta MultispeQ device with Android App on tablet

Sorry for the long delay between posts. There’s lots of expectant beta testers waiting for us to get units out so we need to keep up good communication.

Pick and Place machine!

Though a bit late, we have shipped the first few beta (very beta) units! One took measurements of wheat fields in Mexico as part of the Poland Lab, while one is on its way to the Arctic, with two more staying behind at Columbia University for testing by the Griffin Lab. These units were a bit rushed, because of time constraints, but overall we learned a lot from the experience. The pick and place machine has FINALLY been delivered (see image), so that will dramatically speed up our production of the circuit boards and therefore shipments of the MultispeQ. Will have more news on that soon.

Also, I want to let everyone know that we’ll be at many a Maker Faire this summer, starting with the Ann Arbor mini Maker Faire May 10th (already happened, it was awesome), then the Bay Area Maker Faire in SF May 17th and 18th, and finally the Detroit Maker Faire July 26th and 27th. We’ll have devices on hand and we’re going to try to run mini experiments at the Faire which should be a lot of fun. If you live nearby, please come and check it out!

 

 

 

Ok – here’s a recap of what we’ve been working on since the last post:

Recent Improvements

1) Power consumption and power on/off. Yes, the devices need an on/off switch (duh), and we didn’t really plan for that until recently. Robert (on the second try) designed a very nice switch, which also includes the ability to shut down power via bluetooth or serial communication, which will allow the unit to save power and prevent users from draining the battery. It has another switch for a low power mode which further saves power.

2) Putting the “synQ” in PhotosynQ. We have a lot of software to develop to create the full circle of connections (MultispeQ device to phone app / chrome app to database to web) for all of the key components involved in taking a seemingly simple measurement (protocols to tell the device what to do, macros to calculate useful values from the raw output, project information, user information, device ID…). Taken together, the software is written in javascript, java, ruby, html, and c++ between the web, phone, browser, and device, so making everyone talk nice to each other has been a tricky task.

So I’m very happy to say that we’ve done it! Rather than write about it, I put together a quick video showing the different components, and how they connect. Note – the UI for most of this stuff isn’t completely finished, but all the key connections in place so that projects, measurements, macros, and users truly synq together.

3) Data caching – no need to always be web-connected. All of our first few users required that the device would still work even when not connected to the web. We knew this was important, but were planning to push it off down the road – needless to say we got it done. Now, if you’re in the Amazon rain forests and you want to still take measurements as part of your “Bioprospecting for amazing plants!” project but you have no internet, not problem – the data you collect is cached in the phone and sent to the database once you arrive back at camp (or wherever you have wifi).

4) Improving chlorophyll fluorescence measurements. Making better measurements and calibrating the device will be an ongoing process, but we made some important improvements to the Phi(II) measurement, which is often used in other comparable instruments including the ‘gold standard’ LiCOR. Phi(II) requires that the ambient light is measured, and then that light intensity is mimicked inside the leaf chamber, so the leaf can’t tell that you just clamped it receiving the same amount of light. We calibrated the light intensity sensor and actinic lights and managed to get it working. It’s fairly rough, with only about 20 light levels between 0 – 2000 uE and some minor variation between devices, but it works pretty well.

On the horizon

1) Finishing touches on the data analysis tool. Sebastian made an awesome data analysis tool that we can’t wait to get into the world, but it’s been slow integrating it into the new website.

2) GPS data for all measurements. Right now, GPS data is not included in the Android app – so you can’t make awesome maps of everyone you’ve taken measurements. Obviously, we need to fix that.

3) Enabling users to create custom measurements. Currently, only admin users can create custom measurements (aka protocols) and macros.

4) Easy measurement creation tool. If users will be able to create their own protocols and macros, then we need a nice UI to make it simple and intuitive, as the syntax of communication with the device is, well, not very pretty or intuitive. Sebastian cranked one out last week, and it’s a really good start. Here’s a quick snapshot of what it looks like so far. You drag and drop the variables, change the settings, and viola! you have a protocol JSON which you can use in your next project. This is pre-release but it’s moving fast, hope to have it up in a month.

Sebastian’s protocol creator to make your own custom measurement protocol for the MultispeQ

5) Real-time data logging for environmental measurements. Though designed primarily as a handheld device, you can do long-term data logging with the MultispeQ. As such, Sebastian included a real-time data logging feature in the Chrome app. So if you’re measuring CO2 over the course of a day, you can see each measurement as soon as it’s created and graphed before your eyes, instead of having to wait until the very end of the day.

6) More direct applications for MultispeQ measurements. As a lab of researchers who study photosynthesis all day, the question of “so what can I DO with all these measurements?” sometimes falls on deaf ears. But we’re working to change that by identifying more concrete and clear use cases for chlorophyll fluorescence, SPAD, and other measurements taken by MultispeQ. Professors Dave Kramer and Wayne Loescher have identified a pretty clear relationship between drought tolerance in beans and a special type of chlorophyll fluoresence measurement which is pretty easy to take. This

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could be extremely useful for breeding applications, as you could select for varieties of interest much earlier in their life cycle.

Finally, I want to say thanks to Chris and carrythewhat.com who’s been shipping us MultispeQ cases he’s been 3D printing, and has helped make sure that our cases are 3D printable on a standard plastic extrusion printer (rep rap, maker bot, etc.).

 

Killing plants for fun and science

In getting ready for our trip to the Open Hardware Summit, we wanted to create some short wizbang experiments to show what Photosynq can do. These experiments are focused around measuring pulse modulated fluorescence (find more about it on our ‘how it works‘ page, or this more in depth but accessible summary), which can distinguish between stuff that’s has active photosynthesis (like plants and things that are alive) from stuff that just fluoresces (like white paper and laundry detergents). We also want to create more experiments to show off the CO2 sensor and other stuff, but this is what we’ve got so far.

(Sebastian Kuhlgert and Kent Kovac from Kramer Lab came up with experiments, so thanks!)

Surprise – it’s alive!

Fruits and Veggies… alive or dead?

We wanted to see if fruits and veggies were 1) photosynthetically active at all (meaning, they were absorbing light and doing something with it), and 2) active even after they’ve been picked (sometimes many days after. Sebastian went and got an old pepper and a fresh pepper, an apple, some grapes, and some spinach and we ran some tests. The ‘how alive is it?’ value is called Phi(II) (this is the proportion of light absorbed by PSII which is used for photochemistry – you can read more in depth about chlorophyll fluorescence and ways to quantify it here). The ‘amount of fluorescence’ value is the absolute fluorescence response, which relates to the quantity of chlorophyll (it’s a relative value, not absolute, and it’s not perfect and we’re working on making it better – but let’s work with it for now). Here’s the results:

Blue bars are Phi(II) (ie how efficient is photosynthesis) and red lines are how much chlorophyll is there (these are relative values only)
Blue bars are Phi(II) (ie how efficient is photosynthesis) and red lines are how much chlorophyll is there (these are relative values only)

As expected, the new pepper was more photosynthetically efficient than the old one… but the old one had a higher chlorophyll concentration which was surprising. The grape was reasonably efficient, but didn’t have much chlorophyll (you could have guessed that – grapes aren’t very green and they are mostly water). I was surprised that the spinach didn’t have the highest levels of chlorophyll – it was certainly very green, though it is relatively thin compared to a pepper skin so perhaps that accounts for the difference.

Simple test and simple results, but they raise a lot of questions that even hardened plant scientists will hem and haw about!

Spinach is alive, but chlorophyll by itself is just another protein

Most of plant fluorescence comes from chlorophyll, which is the main component of the antennae which gather light. However, just because something fluoresces doesn’t mean it’s alive! In this experiment, we took a spinach leaf and measured Phi(II) and our relative measure of chlorophyll concentration. Then we mashed up the leaf and mixed it with 80% ethanol (you could use rubbing alcohol or nail polish remover). Then we poured the mixture through a paper towel (coffee filter would also work) so that only a clear green liquid remained. The alcohol removed the chlorophyll and the sieve prevented any whole cells from getting through – so we’re left with a chemical soup containing lots of chlorophyll.

So – what happens if we compare Phi(II) and chlorophyll concentration on the original leaf versus the alcohol/chlorophyll solution?

The spinach leaf is still using the photons to do useful work... the chlorophyll isn't, though they both produces roughly the same fluorescence
The spinach leaf is still using the photons to do useful work… the chlorophyll isn’t, though they both produces roughly the same fluorescence

As you’d expect – the spinach leaf is still using the photons to do useful work (high Phi(II))… but the chlorophyll/alcohol solution isn’t (near zero Phi(II), though they both produces roughly the same fluorescence (red line). This is relevant because fluorescence measurements from satellites can measure absolute fluorescence values, but not photosynthetic efficiency – so it’s valuable to have something like Photosynq to provide additional information on the ground.

Hearing plants cry “Heeeeeelp!”

Plants can’t talk, so when they are stressed (not enough water, too much salt, too hot, too cold, etc.) it’s sometimes hard to tell. Photosynthetic efficiency (Phi(II)) is a good indicator of stress (there’s many papers describing this, see here, here, and here for a few), so it’s a good identified of when plants are in trouble.

So we picked two prickly lettuce plants from the garden and stuck them in water. We added a bunch of salt to the water of one plant, and measured Phi(II) and relative chlorophyll content in both plants over a few days. Here’s the before and after:

In the beginning, there were two prickly lettuce plants…

but Greg and Sebastian smote one with salt!

The results show that not only was the plant wilting, but it’s photosynthetic efficiency was dropping too. It’s like if someone made you drink a bottle of absynthe and then go play in a soccer game – your performance would suffer!

The salt-treated plant (blue line) sees a big drop in photosynthetic efficiency over a couple days
The salt-treated plant (blue line) sees a big drop in photosynthetic efficiency over a couple days

In some ways, this experiment could be a lot more interesting – you can clearly see the plant leaves wilting, so what’s the point of taking the additional measurement with Photosynq, right? In this case that’s true, but out in the field it’s not always obvious that plants are under stress and the visual effects of stress (wilting, loss of color, slow growth) may be delayed by several days. Measuring photosynthetic efficiency directly will identify stresses much sooner.

We’ll have all these experiments plus (if we can get our act together) a few more by OHS on Friday. If you are going, come visit our demo!

Measuring algae with the Photosynq

There’s tons of new stuff, but to start here’s a quick list:

1. From Greg’s nerd nite talk we scooped up Talia, a new volunteer helping develop the app! And we spoke at MSU Global‘s Fanning the Flames about our project and the concept of open commercialization generally (click here for a link to the video) – from there we picked up Patrick Hayes who’s going to help us with marketing and social media!

2. We’re laying the groundwork with MSU to figure out how we’re going to get the finished unit out into the world, patent and license free.

3. The Photosynq device can now communicate with some (not all) cell phones and tablets via USB serial port, using a program like Slick Labs USB to Serial program, which allows you to both send a signal and receive a signal to the Photosynq. And we’ve got a few additional interesting protocols which are still under development but show a lot of promise.

4. We’ve got some great looking data by taking photosynthesis measurements of our algae bioreactors. We can clearly see the difference in the rate of photosynthesis from the top of the column (where it’s very bright) to the bottom (where it’s dark). This is information that no one has every really seen in this way. Previously, you had to take individual samples from each level and measure them in an external machine. Now, we can continuously measure photosynthesis throughout the culture in real time.

The setup is a bit messy…

There is actually a photosynq device and a photobioreactor somewhere in this picture
There is actually a Photosynq device and a photobioreactor somewhere among the cord maze…

But the data looks pretty good! Dave and Ben recently presented some initial findings at the International Conference on Algal Biomass, Biofuels, and Bioproducts. You can always keep up with the science end by checking the documents at our project page at Open Design Engine here. Some stuff gets delayed to go to professional journals, but I do the best I can to shovel stuff straight on to ODE as fast as possible!

But we still have a ways to go before it’s as clean as the data from the benchtop fluorescence unit in the lab – in the graph below you can see that the Photosynq signal is much noisier than the benchtop unit called Ideaspec (the detector response is much wigglier — don’t mind that the size is different – the key is the noisiness).

The basic response is similar (low signal, high signal, low signal) but the photosynq line is noisy)
The basic response is similar (low signal, high signal, low signal) but the photosynq line is noisy)

Ok – that’s enough for one post. More soon!