test of an in vivo method to detect chloroplast division...

hloroplasts — the green organellesfound in plant cells — are central tolife on Earth because of their photo-synthetic functions. The leaves of

higher plants are packed with them. However,the seeds from which these plants grow com-monly have few or no functional chloroplasts.Chloroplasts have a separate genome thatcodes much of this organelle’s function. Thuschloroplasts in plants must arise through re-peated chloroplast division.

Chloroplast division occurs primarily indeveloping leaf mesophyll cells (1–4). Herechloroplast division is essential to mesophylldevelopment (5, 6). References 7–10 list theenvironmental factors that influence chloro-plast division, such as cell size and nuclearploidy. References 11 and 12 discuss the envi-ronment of the leaf, especially light quality.

Although it is clear that chloroplast accu-mulation in developing leaves enormously af-fects photosynthetic production, the controlof the chloroplast division process itself is oneof the least understood areas of chloroplastbiology (13). This situation calls for improvedtechnology to facilitate not only more rapidunveiling of basic understanding, but also toenable and make practical investigations ofhow chloroplast division relates to the myriadspecies, germplasm, and conditions of cropproduction.

In addition, a technology has arisen inwhich medically useful proteins are found tobe more readily biosynthesized in chloro-plasts than in the nuclear-directed part of theplant cell (14). Thus, for all these scientificand practical ends, instruments for rapid de-termination of chloroplast division in vivo arerequired.

In our laboratory, in a fortuitous explo-ration of imaging photosynthetic fluorome-try of Arabidopsis mutants, a novel techniquearose that appears to very rapidly measurechloroplast division in vivo. Preliminarystudies employing diverse methods — in-cluding a germplasm approach using charac-teristics of diverse plants, in vivo spec-troscopy, and confocal microscopy —suggested that this is a viable goal. Biophysi-cal considerations, statistical theory, andpreliminary experimental evidence supportthis hypothesis.

However, all this was not on our mindswhen we stumbled on our new technology.We were investigating variegation mutants ofArabidopsis thaliana. In plants, as in animals,normal cellular differentiation depends oncoordinated interactions between the nuclearand organelle (chloroplast or mitochondria)genomes (23). Genetic dissection of plantvariegation mutants is a powerful means togain insight into these poorly understood in-teractions (7).

We worked with Arabidopsis mutants withleaves that typically contain green sectionshaving a full complement of normal chloro-plasts with high levels of chlorophyll fluores-cence. Most notably, these leaves also havesections of leaves that look white or yellow.

The cells in the white or yellow sectors haveplastids that, for the most part, lack chloro-phyll. In these sectors there are, at low densityand in small number, some plastids withchlorophyll fluorescence. This suggests thatthese cells lack some aspect of nuclear–chloroplast function in a way that manifestsitself, either directly or indirectly, in pigment-deficient plastids.

Spectrofluorometry

Test of an In Vivo Method to Detect Chloroplast Division in Crop PlantsPart I: Discovery of the Phenomenon

Ping Zheng, Carolyn Wettzel, Karim Ammar, Anne-Marie Michelle Girard, SteveRodermel, David R. Thomas, Li Ning, James B. Callis, Gerry E. Edwards, and LarryDaley

This article describesa novelspectrofluorometricmethod thatapparently allows forin vivo observation ofdivision ofchloroplastpopulations in leavesof Arabidopsisthaliana.

C

16 Spectroscopy 17(4) April 2002 www.spectroscopyonl ine.com

Test of an In Vivo Method to Detect Chloroplast Division in Crop PlantsPart I: Discovery of the Phenomenon

Ping Zheng, Carolyn Wettzel, Karim Ammar, Anne-Marie Michelle Girard, SteveRodermel, David R. Thomas, Li Ning, James B. Callis, Gerry E. Edwards, and LarryDaley

April 2002 17(4) Spectroscopy 17

Spectrofluorometry

The method we used is an imagingfluorescence technology (24). Photo-synthetic tissues generate changing pat-terns of fluorescence (Kautsky tran-sients) that occur immediately afterillumination of a dark-adapted leaf.

Relationships between time-dependent fluorescence maxima andtime taken to reach quasi-steady-stateare very sensitive to changes in plantphotosynthetic function. The photosyn-thetic function of plants, while not asclosely related to crop production as wasformerly believed, is without doubt es-sential to agricultural production. Thus

fluorescence pa-rameters are im-portant indicatorsin the agriculturaland plant sciences.

ExperimentalPlant materials. Thematerial wasgrown in thegreenhouses adja-cent to the labo-

ratory, sampled with permission fromsome of the material grown by other re-searchers at Oregon State University(OSU, Corvallis), or sent by expressmail from other researchers across theUnited States.

The Arabidopsis thaliana used for theseexperiments were prepared and grown bySteve Rodermel at Iowa State Universityof Science and Technology (Ames).These materials consisted of grown Ara-bidopsis plants. The Arabidopsis thalianagermplasm used was V28, spotty andwild type. Plants were shipped, carefullyprotected and by express mail service,from the Iowa laboratory site to the OSUlaboratory site. Once the plants arrived atOSU, they were carefully unpacked andallowed to grow overnight to permit re-covery of photosynthetic function fromshipping stresses.

Leaves were sampled and then ana-lyzed by the imaging fluorometer. Wenoted the growth details with care;these Arabidopsis thaliana leaves weretaken at zero, four, and eight days afterarrival from Iowa. Mutants and theirgrowing conditions have been describedpreviously (25–33).

When we grew our own plants weused OSU soil mix and Osmocoteslow-release fertilizer in amounts andrelease times appropriate for eachspecies. The greenhouse crew wateredeach plant twice daily and controlledany pathogens and pests that couldcause problems.

Fluorometric analysis followed Ninget al. (24). Our laboratory developed animaging fluorometer/spectrophotome-ter (24, 34–39) that can quantify, intwo-dimensional space, the fluores-cence characteristics of leaves. This in-strument detects changes related to thephotosynthetic function and pathologyof plants. Our imaging fluorometeruses a charge coupled device (CCD) asdetector element, acquiring spectra for31,680 positions per sample (24).

This instrument has had a goodnumber of applications. These applica-tions include using in vivo fluorescentimaging for detection of damage toleaves by fungal phytotoxins (40); therecovery of digital information storedin living plant leaf photosynthetic ap-paratus as fluorescence signals (41);imaging of water in living leaves, andso forth (38, 39, 42).

The instrument has been featured ina number of places, such as front cov-ers for scientific journals (43–45). Itwas discussed in reference 46.

Data processing. However, in additionto the expected results characterizingthe mutants, we found photosyntheticdiscrete populations with diverse meanlevels of photosynthetic functionalityin the chloroplasts of both wild-typeand mutant plants. Our data demon-strate the “statistical reality” of this pre-viously undescribed phenomenonusing a statistical deconvolution pro-gram specifically developed for thispurpose.

Each image contains 31,680 datapoints (pixels). In their original distri-bution, these data points generated theimage of the chosen leaf, showing thedistribution of photosynthetic effi-ciency (Y�). Here the data points are re-organized for analysis.

First, sample leaf data points withvalue 0 — that is, data points having no

The state of theart of chloroplast divisionobservations is not yetmature

DNA technologycannot be useddirectly to measurechloroplast divisionbecause there isnot a linear

correspondence of chloroplastgenome to chloroplast numbers.

Scott et al. (15) describe chloroplastDNA division in a young spinach leafcell in rapid division as having 10 or soimmature chloroplasts with 7–100copies of chloroplast DNA. As celldivision ceases in most nuclei, theDNA remains diploid; however, thereis a period of continued division ofchloroplast DNA giving increased DNAper chloroplast. Then, as the leaf is stillexpanding, chloroplast DNA divisionceases, and chloroplast division andcell enlargement become the majorprocesses. At the end of the process,the mature spinach leaf cell contains25–50 chloroplasts with about 20–40copies of chloroplast DNA perchloroplast.

From the above, it is clear thatchloroplast genomes are multiple andthe number of genomes not constant.Thus, measurement of chloroplastDNA is not an adequate measure ofchloroplast division.

Another consideration that detractsfrom DNA measurement as a measureof chloroplast division is thatchloroplast components can bedirected by nuclear genes, such as isthe small Rubisco subunit; therefore,probes used for this purpose mustrecognize bona fide chloroplast genes.Thus, it would seem that DNAtechnology is not, as yet, readilyapplicable to test our technology.

Microscopy has limitations. Untilrecently, the main methods used to

Continued on page 19

www.spectroscopyonl ine.com18 Spectroscopy 17(4) April 2002

Spectrofluorometry

photosynthetic activity — were re-moved. The remaining data record onlythe photosynthetic active surface of theleaf sample. Data from each image wereplaced in order of rank of Y� along the xaxis to evaluate statistically the distribu-tion of Y� data. That is, the density ofdata points at that value of Y� (y axis)was plotted against rank of Y� (x axis)of that data point.

All this generated curves with Y� asthe y axis and rank of Y� as the x axis.The resulting curves were deconvolutedusing the the expectation maximization(EM) method (see second paragraphfollowing and [47]) to seek the numberof normal curves found in each popula-tion distribution curve of each leaf.

In this circumstance, one expects thatonly one population of Y� in each leafin the resulting curve will follow a nor-mal distribution. If the resulting curvecontains a number of normal curves,then there is more than one populationof Y�. Although technically speaking,what we are following are data points,not chloroplasts, we are, in essence,sampling the Y� of groups of chloro-plasts across the leaf, seeking differencesamong them. When we find such differ-ences, it is reasonable to imply thatthere are different groups of chloro-plasts with different properties.

Program for data analysis (curve deconvolu-tion). One of the most computation-intensive problems is to calculate amaximum-likelihood estimate for amixture of normal distributions. This

can be thought of as parametric estima-tions of likelihood functions. Maximiz-ing these likelihood functions is com-plicated by singularities and numerousspurious maximizers. Here we used theEM algorithm, which is a technique forfinding maximizers of likelihood func-tions (47). The program running theEM algorithm was written using a nu-merical software package called MAT-LAB (The Math Works, Natick, MA).

This iterative EM algorithm is ex-tremely reliable and usually finds the“good” maximizer from most reason-able initial guesses. However, it is veryslow in cases where there is consider-able overlap between component nor-mal distributions.

We set up a model in which the re-sultant distribution is considered as a

mixture of normal distributions. To dothis we wrote a novel program in MAT-LAB using the established formulas andmathematical conventions. In these for-mulas we consider the unknown distri-bution as a mixture of a number ofsubdistributions from 1 to m, where mis the number of normal componentsin the mixture (Ping Zheng, David R.Thomas, and Larry Daley; manuscriptin preparation).

Confocal microscopy was done onthe OSU instrument, a Leica (Hiedel-berg, Germany) TCS 4D, using a AgKrlaser scanning at 568 nm.

Results and DiscussionWith the objectives and intent previ-ously described, we estimated photo-synthetic efficiency measuring various

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chloroplast efficiencies in Arabidopsis spottymutants: (a) and (b) show data before statistical

transformation; (c) and (d) show statisticallytransformed data; (a) and (c) show curve fitting

to histograms in (b) and (d).

Figure 2 (bottom right). Ranked andstatistically processed data showing distinct

populations of chloroplast efficiencies in wild-type (nonmutant) Arabidopsis: (a) and (b)

show data before statistical transformation; (c)and (d) show statistically transformed data.

Upper frames (a) and (c) show curve fitting tohistograms in lower (b) and (d) frames.

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Spectrofluorometry

points of the transient spectra of fluo-rescence released on the onset of pho-tosynthesis. These data were collectedwith a (now quite dated) CCD with31,680 data collecting units, or pixels.The data gathered were then processedthrough a patented algorithm, generat-ing an image estimating the photosyn-thetic efficiency across a section of leaf(24, 34, 35, 40–42, 48).

Photosynthesis occurs at numeroussites in each active chloroplast. How-ever, what our instrument detects is anaggregate image of the summed func-

tion of each set of chloroplasts that im-pinge on each pixel.

The data are presented as a false-colorimage. The intensity of function is color-coded as an essentially continuous lightspectra function, in which the deepestred represents the most efficient; theranges of oranges, yellows, greens, andblues are progressively less efficient; andblack (no color) is background. In thisimage, nonfunctioning chloroplasts andnonphotosynthetic tissues are perceivedas low efficiency or background colors(24, 34, 35, 37, 40–42, 48).

observe chloroplast division were lightand electron microscopy (10, 16).Using these methods and painstakingwork, a number of characteristics ofdividing chloroplasts were established.

Yet, microscopic observation ofchloroplast division is not one of thesimpler tasks in biology. Physicalobservation of chloroplast division isenormously demanding. Chloroplastsexist as roughly ovoid to tubularobjects moving in flexible undulatingfashion in three dimensions. Thus, inmost higher plant species, it takesdedication, persistence, and a verylarge number of microscopicobservations just to catch and verifychloroplasts in the act of division.

Some investigators have approachedthis problem using a different mutantof Arabidopsis having fewer, butlarger, chloroplasts per cell (17). Usingthese Arabidopsis mutants, it wasestablished thatthese plantscompensate forreduced chloroplastnumber by allowingthe few chloroplaststo grow muchlarger (20�) thanin wild type (7, 17,18). These mutantshave allowed somevery elegant studiesof chloroplastdivision (19);however, practical measurement ofchloroplast division by this approachis limited to the mutant in question.

Confocal microscopy, anincreasingly used technology, (20, 21)allows one to observe and follow theshape of the chloroplast through the zdirection (z��depth [22]); however,this technology requiring large fixedinstrumentation is still verycumbersome for uses such as thesethat require repeated measurements.

Figure 3 (top). Statistically processed curves showing dual populations of chloroplast efficiencieswithin mutant and wild-type leaves.

Figure 4 (bottom). A larger set of data also showing statistical analysis from second variegatedmutant (V-28).

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Continued from page 17

For this particular series of experi-ments, seeking differences in photosyn-thetic efficiency between the mutantand the normal parts of these Arabidop-sis, we reprocessed the efficiency data.We ranked the data with respect to effi-ciency on the x axis, with the y axis asthe number of pixels at that efficiencyrange. This approach generated normalcurves of the efficiency of the chloro-plasts within each image. Our originalintent was, in this way, to measure theproportion of mutated tissue and theamount of normal tissue.

Initially, we tested the data statisti-cally by rigorous statistical analysis forsubpopulations. This statistical ap-proach verified the presence of these

subpopulations. In these mutants weoften, but not always, saw two popula-tions with different photosynthetic effi-ciencies. That made us very happy, be-cause it seemed to explain thedifferences between the dark green andthe much lighter white or yellow sec-tors of the mutation plant’s leaves (forexample, spotty mutant data presentedin Figure 1).

Figures 1a and 1b are the curves andhistograms for raw data, and Figures 1cand 1d are for statistically transformeddata. If the data were random, Figure1c and especially Figure 1d would notbe expected to show this binary distri-bution. This seemed to confirm ourinitial working hypothesis because, at

least at first, most of the wild typeseemed to have merely one peak(Figure 2).

In Figure 2d, application of the de-convolution program to these datashows that, except for a hint of a lowpeak at low density, the data support asingle normally distributed populationof Y� (estimated photosynthetic effi-ciency) (35) data. This suggests thatone major population of chloroplasthas a common photosynthetic effi-ciency. Because this distribution wasmost common in wild type, we werehappy to forget this little hint of some-thing else.

At that point we expected wild typeto have a single, normal curve and mu-

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Figure 5. Plot of in vivo attenuance of photosystem I (estimated as 684–800 nm from [56]) viz. position on Amaryllis leaf, compared with pattern ofphotosynthetic efficiency in each part of leaf.


Spectrofluorometry


Spectrofluorometry

tants to have two. Nice, tidy, and logical— or so it seemed then.

Thus, at first it seemed safe to ignorethe small suggestion of a second peak atlow Pm (statistically transformed pho-tosynthetic efficiency), after analysis

shown in the wild-type in Figure 2d(34–35). However, as our work pro-gressed and data accumulated we foundsome wild-type data that had peaks atlow Pm. These peaks now were impos-sible to ignore (for example, see Figure 3c).

Thus we embarked on a wider exam-ination of mutant and wild type spectrausing the same statistical deconvolution(Figure 4). With these data webegan to clearly see two popula-tions of chloroplasts with differentefficiencies not only in other mu-tants but also in wild type Ara-bidopsis plants (Figure 4). This wasquite disturbing, very puzzling, andnot especially tidy, for it disagreedwith our initial hypothesis.

We could not attribute the differ-ence in populations to an artifact ofextraction because the technologyused here was an in vivo method,and no extraction procedure is in-volved. What we see with this in-strumentation represents the totalpopulation of functional or semi-functional chloroplasts of the areaobserved.

We asked ourselves many ques-tions. For instance, were we reallyseeing two populations, or were weseeing the same kind of populationat two different parts of its cycle of

development? In this test model sepa-rate new populations of chloroplastswould be increasing, maturing, and

then decreasing inefficiency with age.This particular hy-pothesis was notvery credible be-cause one wouldexpect that such acircumstancewould result in notjust two, but inmany, populations.

However, theprocess of discov-ery was complex,with hypothesisafter hypothesisfailing to give rea-sonable explana-tions of the dataobserved.

For instance, weconsidered the cir-cumstance thatsome types ofplants, known asC-4 plants, are long

known to have different chloroplasts inthe mesophyll and bundle sheet cells(49). The trouble with this explanationis that Arabidopsis are not C-4 plants;C-4 plants, it turned out, had evenmore complex distributions of chloro-plast efficiencies (this will be discussedin the second article in this series).

We were getting nowhere looking foran explanation; thus we had to be surethat our observation was not merely atrait particular to our first experimentalplant Arabidopsis. This unusual weed isknown to have a reduced genome andto grow very quickly, and this mightvery well be a new particularity of theweed (50). We explored other species; itturned out that the phenomenon oc-cured in other plants. An example ofthis is shown here for Amaryllis(Figure 5).

As Figure 5 illustrates, the phenome-non is indeed general, and is limited tothe base of the Amaryllis plant wheregreening and, thus, chloroplast divisiontakes place.

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Figure 6. Plots of photosynthetic efficiency versus time (left; figure modified from [51] ) andfrequency versus chloroplast area (right; figure modified from [7] ).

Were we really seeing two

populations, or were we

seeing the same kind of

population at two different

parts of its cycle of

development?

Were we really seeing two

populations, or were we

seeing the same kind of

population at two different

parts of its cycle of

development?


Spectrofluorometry

This information suggested that anexplanation based on chloroplast divi-sion was possible. Research using anobscure algae, Heterosigma carterae, hasdemonstrated that when chloroplastsdivide, photosynthetic efficiency de-creases (51). This gave a theoretical ex-planation that fits our data as is shownin Figure 6.

Now the explanation that what weobserved was chloroplast division was

quite novel and, if correct, quite impor-tant. Thus we applied for a grant; thereviewers were not gentle, but they didraise important criticisms.

One criticism of this chloroplast divi-sion explanation was that the number ofchloroplasts per pixel of CCD covered byour detector was too large to explain thestatistical discontinuity of our data.

This criticism would indeed be validif we were observing all chloroplasts.

We did a simple calculation of chloro-plast size (see data in Figure 6) usingthe data on chloroplast size of Pyke andLeach (7). This calculation showed thatbecause the CCD pixels in our deviceare set up to cover 12,626 �m2

, eachpixel could gather signals from as manyas 63 200-�m2 chloroplasts. Such agroup size of chloroplasts would be ex-pected to generate an averaged effi-ciency signal rather than a biphasic one,damping out any differences. How then,the critics asked, could we see differentpopulations with our device?

This question led us to reconsiderbasic concepts of plant function. Onesuch assumption about photosyntheticfunction is that it is continuous. Thefact that photosynthetic function iscontinuous on the macroscale of thewhole leaf is easily provable (for exam-ple, see references 52–54). However,taking this assumption to the chloro-plast level is not readily justified be-cause measurements of the photosyn-thesis of a single chloroplast aredifficult.

Although measuring a single chloro-plast cycle is difficult, it is much easierto get a good number of chloroplastssynchronized, and this is almost asgood for some purposes. What we (24),and many others before us (as cited inthis article), do when we measurechloroplast function is keep the leaf inthe dark because then, after a few min-utes of darkness, many of the chloro-plasts have reset to the ready state.

When the instrument suddenly turnson the lights, these reset chloroplastsstart from that point. The short wave-length excitation light impinges on thephotosynthetic apparatus, which firstresponds by fluorescing strongly atlonger wavelengths. Then, very rapidly,the fluorescence decreases as thechloroplast uses more of the incominglight to do chemical work (for example,see references 24, 41, 42, and 48). How-ever, high background levels of fluores-cence indicate that some chloroplastscontinue to fluoresce and thus areinactive.

Another line of evidence (see “Fac-tors that limit the number of functionalchloroplasts” sidebar) is that one can

Factors that limit the number of functionalchloroplasts

CO2 restrictions. Low CO2 levels limit chloroplast function. Thus,when CO2 limits photosynthesis, some chloroplasts do not haveenough of this gas to function.

Shading. Chloroplasts can stack in leaf cells. For a chloroplast tofunction, it must be accessed by light for the machine to detect it.Thus, if there are too many chloroplasts to function at our lightintensity, and they self-shade, we only work with the ones thathave enough light to function.

Reaction center recovery. Photosynthetic systems in chloroplastsdo not work all the time they need to for recovery. Use of light byliving leaves is a slower process than many imagine. It is true thatthe first steps of the light reactions are extremely fast and thesefirst steps occur in picoseconds. However, it is known that it takesabout 12 s for the photosynthetic process to fully function, and ittakes about 3 min for a leaf to reach steady state (24). However,

this steady state still has considerable fluorescence indicating that, at any given time,nonfunctioning chloroplasts are present.

In experiments in which we measure the recovery time of leaf photosyntheticfunction by the disappearance of an imposed image, it takes about 8 min for theimage to essentially disappear, and before photosynthetic function is restored to allchloroplasts in sample (41). This observation also demonstrates that chloroplastsneed to recycle.

Another observation is that leaves have a high optical density (A) at 684 nm, thepeak absorption for photosystem II. This A is often, as in wheat (not shown) in theorder of 3–4 A (or more) at the 684 maximum (55). 1A absorbs 90% of the light, 2A99%, 3A 99.9 %, and 4A 99.99%, yet A is proportional to concentration. Because A isdirectly proportional to concentration, a 4A leaf has, at least for some structuralfactors, four times the chlorophyll of a 1A leaf.

Thus, for a 1A leaf to become a 2A leaf it has to double its chlorophyll for a returnof 9% increase, and it has to triple its chlorophyll for a return of 0.9% increase. Thisraises the question, why should leaves expend that kind of energy for such a smallreturn? A more rational explanation is that the leaf chlorophyll is partitioned intophotosynthetic units (here chloroplasts) that function at different times. And at anygiven time, most chloroplasts are recovering while only a few are in active light-gathering function.


We infer from our data and

logical analysis that what

we are seeing is chloroplast

division.

We infer from our data and

logical analysis that what

we are seeing is chloroplast

division.

Spectrofluorometry

working a very phylogenetically distantspecies, Heterosigma carterae (51).

Preliminary experiments with otherspecies using this method, along withconfocal microscopy, support the ideathat we are observing chloroplast divi-sion. This will be discussed in detail inthe second part of this series.

AcknowledgmentThe authors thank Don Powell and hisfamily, owners of Garland Nursery(Corvallis, OR), for the kind donationof numerous Amaryllis bulbs.

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use this phenomenon to generate im-ages; however, these images fade gradu-ally in about 8–10 min (41). The grad-ual nature of the loss of these imagesinforms us that not all chloroplasts re-cover immediately. Chloroplast func-tion — even under our conditions ofprior dark adaptation — and suddeninflux of light are not synchronized;thus, for each chloroplast, light use isdiscontinuous.

An independent indication thatchloroplasts do function intermittentlyis that, in vivo, sufficient chlorophyll toabsorb 99.99% or more of the imping-ing light may be found in some leaves(see “Factors that limit the number offunctional chloroplasts” sidebar). Thisabsorption level can be so completethat, were chloroplasts collecting lightat all times, this excess synthesis oflight-collecting chlorophyll moleculeswould be an absurdly inefficient use ofa plant’s energies.

This makes it easy to see how, in suchcircumstance of low active chloroplastcount per pixel, dividing chloroplastswith low efficiencies would be detectedin a proportion of the pixel field.

So the number of active chloroplastsper pixel of the CCD may well be smallenough to make the diversity amongsmall populations of chloroplast thatwe observed, consistent with theoreticalconsiderations.

Conclusions Statistically we know that the existenceof distinct populations of chloroplastefficiency is a real phenomenon, occur-ring both in wild-type and in varie-gated mutant Arabidopsis.

We infer from our data and logicalanalysis that what we are seeing ischloroplast division. This is consistentwith data from another laboratory


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Ping Zheng is a graduate student in theDepartment of Horticulture, OSU (Corvallis,OR, 97331); she may be contacted by e-mailat [email protected]. Carolyn Wettzel is a recent graduatewith a Ph.D. from in the Botany Department,Iowa State University (Ames). She may becontacted in care of Steven Rodermel,e-mail: [email protected] Ammar is a breeder in theHybrid Wheat Program at CIMMYTInternational, Apdo. Postal 6-641, 06600,Mexico D.F., Mexico; e-mail: [email protected]. Anne-Marie Michelle Girard isfaculty research assistant at the Center forGene Research and Biotechnology, OSU;e-mail: [email protected].

Steven Rodermel is a professor inthe Botany Department, Iowa StateUniversity (Ames); e-mail:[email protected]. David R. Thomas is professoremeritus in the Department of Statistics,OSU; e-mail: [email protected]. Li Ning is a design engineer at Chrontel(San Jose, CA); (408) 544-2171, fax (408)383-0338, e-mail: [email protected]. James B. Callis is a professor in theDepartment of Chemistry, University ofWashington (Seattle); (206) 543-1208,e-mail: [email protected]. Gerry E. Edwards is a professor in theBotany Department, Washington StateUniversity (Pullman); e-mail: [email protected]. Larry Daley is a professor in theDepartment of Horticulture, OSU; e-mail:[email protected]. �

Circle 15

Spectrofluorometry

test of an in vivo method to detect chloroplast division...

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