Anthony’s Science Blog

Microplate OPAME for total free amino acids

I have spent some time over the last few weeks measuring total free amino acids in soil solution collected with microlysimeters. I’m posting some tips here in case it helps anyone else who might be doing this. The abstract of the primary reference paper on this method explains why it is used:

The spectrofluorometric procedure relies on the reaction of free amino acids with o-phthaldialdehyde and -mercaptoethanol [OPAME]. The fluorometric method is much more sensitive (working range 0.1-50M) than typical spectrophotometric analysis procedures for free amino acids which employs the ninhydrin reagent (working range 10-500M). In addition, the method only requires small sample volumes (1-50l), is rapid (1min sample^-1), simple to perform, and linear over a concentration range of three orders of magnitude (0.1-100M).

We have been using this procedure on 96-well microplates and when it works well, it is fantastic because there is only one reagent to add to the samples (OPAME working reagent) and the plates can be read immediately. The reagent is also sensitive to NH₄⁺, but it is much more sensitive to amino acids (e.g., for our Leucine standard, it is ~40x more sensitive to Leucine on a molar basis) so it is easy to subtract out the small contribution of NH₄⁺ to fluorescence.

In the arctic tussock tundra soils I’m working with, we have to run an additional plate with no OPAME reagent (buffer and sample only) in order to subtract out the autofluorescence of the brownish soil water samples that we are collecting from the microlysimeters, but that is fairly easy to do.

The tricky part of this procedure is working with the OPAME reagent because it is a bit fussy. It has a high background fluorescence that changes over time once the mercaptoethanol is added to the OPA. To measure the low concentrations of amino acids in soil solutions, you want to hit the sweet spot where background fluorescence is minimal but reactivity is still high.

My best and admittedly pretty rough guess at this time is that 30-50 hours after adding mercaptoethanol to the working reagent is the best time to use it. This can require some advance planning and we have begun keeping two batches of the stuff so we can have some ready on any given day. (You can regenerate the working reagent by periodically adding more mercaptoethanol).

The other challenge in using OPAME with microplates is that the fluorescence of the samples will decay rapidly (on the scale of minutes) after you add the reagent to them. This means that you have to add the OPAME as simultaneously as possible to all wells of the plates that are being used. Slow pipetting will cause wells on the last-done part of your plate to have inflated values.

Also, it’s very important to draw all OPAME reagent from the same source over the course of pipetting, so don’t refill your pipetting reservoir mid-plate. When using a multi-pipette, it is good to pre-wet the tips by pipetting OPAME reagent in and out a few times before adding the reagent to the plates to make sure volumes are consistent among wells.

The background fluorescence can be minimized by using a low OPAME reagent:sample ratio. We have been using 50 ul of sample and 100 ul OPAME. Even less OPAME could probably be used as I don’t think it’s anywhere close to saturating at these levels. This dramatically improved our results over when we were originally using 20:200.

Hope this helps!

First dissertation chapter in print

Can be found here!

I won’t deny it: this paper is pretty abstruse and is only relevant to a handful of soil scientists who study this stuff. My father-in-law printed a copy to read in the bathroom and honestly I don’t recommend that. That said, it actually does have some new and interesting findings in it.

I examined levels of landscape heterogeneity (quantified using semivariograms) in soil organic matter carbon and nitrogen from two different soil density fractions using soils from Niwot Ridge. The density fractions contain soil organic matter with divergent residence times and thus represent soil organic matter of different ages. Though many studies have examined landscape patterns in soil organic matter, few have examined patterns in the different chemical constituents of soil organic matter. The results contribute to a growing body of evidence that suggests that microbes essentially homogenize the widely varying chemical inputs that plants provide. Pretty cool.

Plots melted out and instrumented

Our experiment is progressing well. Here’s a video of the removal of our fabric on our accelerated snowmelt plots.

About 4 days later, our control plots also melted out and they are now fully instrumented with passive warming chambers and our ‘mantis’ instrument arrays.

Project Snowmelt

On Monday of this week, I arrived at Toolik Field station in northern Alaska (68° N latitude) to begin the field work for my new postdoc. It’s been amazing to see the arctic, to live at Toolik (an amazing research station) and to participate in kicking off a huge collaborative project now dubbed the “Snowmelt” project by the station staff.

I arrived with Mike Wientraub, my postdoc advisor and the head PI of the project, as well as Heidi Steltzer (professor at Fort Lewis College), and Caroline Melle, (grad student at CSU Fort Collins). The first thing we did when we arrived was to set out black fabric on some plots marked for advanced snowmelt treatment. The black fabric absorbs solar energy and melts the snow, thus simulating earlier onset of the growing season. We will also have some open-topped passive warming chambers as a second crossed treatment in these plots. Once these treatments are in place, we will measure a suite of variables quantifying the response of plants and soils.

Even after the first day of deployment, the fabric began to do its job and there was a noticeable difference in snow depth between treatments, as can be seen by the lip of snow next to the fabric below.

It’s been great to start up this new project. Just before I got here, I gave my exit talk in the Ecology and Evolutionary Biology department at CU Boulder on April 23rd, and then defended on April 26th. The thesis was approved unconditionally, so I am officially Ph.inishD! This is good because I can now legitimately call myself a postdoc.

58 days until I submit my dissertation…

…to my committee that is. They will probably have me revise some things at that point, but 58 days is the big huge deadline that I am working toward.

I’m on track for my 58 days. My first chapter has been accepted for publication in Arctic, Antarctic, and Alpine Research. My second chapter is also completed and is being reviewed by my advisor. I plan to incorporate his suggestions and submit that paper sometime this month to the journal Ecosystems. But there is of course a lot still to do. I am working on my third chapter right now, which I will to send to my advisor as soon as it is done. The good news is that I am not teaching and can thus focus solely on this third chapter.

After that I move to Ohio. That’s right: Holy Toledo. I just accepted a postdoctoral position in Toledo with Dr. Mike Weintraub, whom I know from his stint as a postdoc here at CU. Mike, currently a faculty member at the University of Toledo, was recently funded by NSF to examine the effects of altered growing season length on C and N cycling in the arctic. The project is three years in length, based at the Arctic LTER at Toolik Lake, so I’m psyched to be going to Alaska this summer and to be working with Mike, who is a great guy.

Mad Scientist

From an article in this week’s Nature about South Korean scientist Woo Suk Hwang who has been on trial for fabricating his research…

In August, at the last of 43 judicial hearings on the affair, prosecutors sought four years in prison for Hwang. It was the end of an epic 40-month trial that saw the chief judge change three times, heard testimonies from more than 60 witnesses and included some bizarre moments — such as when Hwang claimed he could not account for some of his funding because he had used it to pay Russian mafia for access to the DNA of a frozen mammoth that he hoped to clone.

Wow.

Summer field work

I got married!!!

Pretty awesome. We had a great wedding on September 5th and an equally great honeymoon to Hawaii. Also, Jackie knows me pretty well (of course) and so she made me this awesome nitrogen cake!

But before wedding madness took hold, I had a great summer field season on Niwot Ridge. It was my last field season of my dissertation and I did a vegetation survey of my study area, which was pretty fun. We measured hundreds of quadrats. Here are Riley, Andrea, and Carly with our beloved quadrat:

I can’t wait to put all the data together and analyze it with my nitrogen data. However, I have a bunch of other manuscript and postdoc related work I have to get to before I can do that. Here’s a little friend that we came across! Thanks to Carly for these pictures.

We got pretty good at identifying the plants this summer, even the grasses and sedges, though the Poa spp. are still basically impossible to tell apart, even with the extremely generous help of expert Colorado botanist Nan Lederer. Even for those though, we have a good set of morphospecies which I will go with.

Thanks again to the awesome field crew!!

Ecological Society of America Meeting 2009

I’ve seen lots of good talks this year and have had lots of fun conversations with fellow ecologists that I’ve gotten to know over the years. This evening I will be presenting a poster, which is the same as the poster I presented earlier this year at the Front Range Student Ecology Symposium. I actually kind of wish that I had an updated version of my poster since I have made a lot of progress on analyzing this data since I made the poster, but it should still be fun to chat with everyone.

I’ve seen some good talks this week, including lots of nitrogen talks (of course) and other good global change and biogeochem ones as well.  I especially enjoyed Dan Liptzin’s talk on rapid changes in oxygen levels in tropical soils. He showed us how the oxygen levels (and inversely, CO₂ levels) in soils track the tropical rains in Puerto Rican rainforests. He also showed, however, that within a very similar area, overall oxygen levels were high, low, or variable with no easy way to guess which spots would be which. As always, more research is called for.

The conference is being held in Albuquerque this year. It’s been fun poking around, though I have to say that my general impression is that there are a lot of sketchy characters here. I saw some sketcher harrassing a conference attendee at the bus stop yesterday and my hand was on my cell phone in case the situation worsened (it did not). Also, two nights ago when I approached the house I’m staying at, some random dude hopped up off the porch swing and took off into the night! On the plus side though, I love the little adobe-style houses and I had some awesome tacos yesterday. I unfortunately don’t have any pictures of the city. Gotta remember to bring my camera when I come to these things!

2009 Green Lakes Valley Snow Survey

I didn’t do my own snow survey this year but I had a lot of fun participating in the annual Green Lakes Valley snow survey for the Niwot Ridge LTER. Here’s me with my snow-measuring pole on snow-covered Green Lake 4.

All of these pictures are courtesy of my snow-measuring buddy Jordan Parman, grad student in geography. The hole in the picture formed when Jordan fell through the snow. The lake is of course solid ice below the snow, but there was this weird empty spot running in a sort of snow fracture across the center of the lake. That was pretty cool. Here’s a better picture of the fracture.

Probably the hardest part of the survey was snowshoeing at 11,500-12,000 feet all day long. Here’s me on a slope:

Probably the coolest part was the fantastic scenery in the glacially carved Green Lakes Valley. Here’s Green Lake 3 and Kiowa Peak.

Thanks to the organizers Kurt and Casey for doing a great job and to Liz for cooking. And thanks to all participants for good company. Oh, and also Kathy Clegg of INSTAAR, who baked the large number of delicious pies we ate, which is pretty damn awesome of her. THANKS!

Quantifying spatial heterogeneity

I’ve thought a lot about quantifying spatial heterogeneity over the course of my dissertation. To demonstrate different types of spatial heterogeneity, I made the graphic below. I originally included this graphic in a draft of a paper that’s part of my dissertation. I’m nixing it for the next draft of the paper since the reviewers complained bitterly, one of whom said that “These generalized maps can be found in standard textbooks on geostatistics.”

While the reviewer is correct that maps similar to all parts of this figure can be found in various locations, I don’t think it’s really true that they have been presented together in this way before (post if you find it; I of course won’t rule out its existence elsewhere). But anyway, for me this formulation helps in classifying the different kinds of spatial heterogeneity that can be observed. Maybe it will be useful for others too.

Changes in three different types of heterogeneity are shown: overall variation, the magnitude of spatial autocorrelation, and the scale of spatial autocorrelation. Three corresponding metrics can be used for each of these types of variation: the coefficient of variation (CV) as a metric of overall variation, the relative magnitudes of the modeled variogram nugget and sill as a metric of the magnitude of spatial autocorrelation, and the variogram range as a metric of the scale of spatial autocorrelation. The second two types might not make any sense if you are not familiar with geostatistics. If you are curious, I recommend Model Based Geostatistics by Diggle and Ribeiro (2007).

In the figure, heterogeneity increases from left to right. Only one type of heterogeneity is modified for each arrow. In the three simulated grids on the left, the coefficient of variation increases. In the two upper grids, the scale of spatial autocorrelation is reduced while leaving the magnitude constant; vice versa for the two lower grids. Grids are simulated 50 x 50 Gaussian random fields with the same random number seed, which ensures that differences are due only to parameter choices.