optimized analysis of variable chlorophyll fluorescence in algal physiology under stress conditions

153 Pages·2014·6.25 MB·English

by  Nicole Bobco

OPTIMIZED ANALYSIS OF VARIABLE CHLOROPHYLL
FLUORESCENCE IN ALGAL PHYSIOLOGY UNDER STRESS
CONDITIONS: MEASURING NOTHING WITH CONFIDENCE !
_______________
A Thesis
Presented to the Faculty of the
Moss Landing Marine Laboratories
California State University Monterey Bay
_______________
In Partial Fulfillment
of the Requirements for the Degree
Master of Science
in
Marine Science
_______________
by
Nicole Bobco
Spring 2014
CALIFORNIA STATE UNIVERSITY MONTEREY BAY
The Undersigned Faculty Committee Approves the
Thesis of Nicole Bobco:

OPTIMIZED ANALYSIS OF VARIABLE CHLOROPHYLL FLUORESCENCE IN ALGAL
PHYSIOLOGY UNDER STRESS CONDITIONS: MEASURING NOTHING WITH CONFIDENCE
_____________________________________________
Nicholas Welschmeyer, Chair
Moss Landing Marine Laboratories
_____________________________________________
Erika McPhee-Shaw
Moss Landing Marine Laboratories
_____________________________________________
G. Jason Smith
Moss Landing Marine Laboratories
_____________________________________________
Marsha Moroh, Dean
College of Science, Media Arts, and Technology
______________________________
Approval Date
iii
Copyright © 2014
by
Nicole Bobco
All Rights Reserved
iv
ABSTRACT
Optimized Analysis of Variable Chlorophyll Fluorescence in Algal
Physiology Under Stress Conditions: Measuring Nothing With
Confidence
by
Nicole Bobco
Masters of Science in Marine Science
California State University Monterey Bay, 2014

The detection and determination of viable phytoplankton are fundamental assays in
oceanographic and limnological fields. Assessing phytoplankton physiology allows for
insight into how cells might respond to different environmental or physical stressors, making
it an important subject for ecological research and applied sciences, where the physiological
status of photautotrophs must be known. In particular, maritime operations have been
identified as major vectors in the spread of aquatic invasive species through ballast water
transport. To mitigate this environmental hazard, shipborne systems are required to
effectively sterilize ballast water to comply with emerging regulations regarding the transport
of ballast water. Common oceanographic tools must now be reassessed for their capacity to
quantify the treatment based reduction in viable biomass for organisms, like phytoplankton,
to determine efficacy and compliance of treatment systems with ballast water regulations. In
order to meet the time-cost demands of maritime operations, ballast treatment compliance
assessment methods need to provide rapid test results on site in contrast to gold standard
methods like microscopic enumeration. Pulse Amplitude Modulated (PAM) fluorometry
offers one method to assess the phytoplankton component of ballast water. This technology,
exemplified by the WALZ WATER-PAM, measures chlorophyll fluorescence kinetics to
provide various indices (e.g. F /F ) of photosystem activity, a proxy of cell viability or
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phytoplankton health. Unfortunately this method is optimized for high biomass suspensions
and distinguishing low concentrations of viable cells from inactive cell samples, as required
for ballast treatment monitoring applications, challenges the sensitivity limits of the current
instrumentation package. This thesis describes the development and testing of a series of
custom algorithms developed in MATLAB to enable robust assessment of PAM rapid light
curves trends in order to quantify the absence of biological activity in treated ballast water
samples. The series of algorithms provide a simple user interface and has practical
applications in a wide range of academic as well as regulatory applications.
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TABLE OF CONTENTS
PAGE
ABSTRACT.............................................................................................................................iv
LIST OF TABLES...................................................................................................................vi
LIST OF FIGURES................................................................................................................vii
ACKNOWLEDGEMENTS...................................................................................................viii
CHAPTER
Introduction..............................................................................................................1!
The Invasive Species Problem...........................................................................1!
Current Methods to Measure Viable Biomass...................................................2!
Chlorophyll A and Fluorometers..................................................................2!
Chlorophyll-Based Most Probable Number (MPN)....................................3!
Microscopic Enumeration............................................................................3!
Variable Fluorescence........................................................................................4!
Light Budget in Photosynthetic Cell............................................................4!
Variable Fluorescence..................................................................................6!
Measuring Variable Fluorescence................................................................6!
Quenching Parameters.................................................................................8!
Differences between PAM and FRR Fluorometers...................................10!
Purpose.............................................................................................................14!
Thesis Questions..............................................................................................14!
Methods..................................................................................................................15!
Rapid Light Curve Experiments......................................................................15!
Protocol......................................................................................................16!
Concentration Experiments........................................................................17!
Blank Determinations................................................................................19!
Results....................................................................................................................19!
Series of Custom Algorithms...........................................................................19!
Organization and File Retrieval Algorithms..............................................22!
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Rapid Light Curve Data Processing Algorithms.......................................22!
Rapid Light Curve Statistical Analysis Algorithms...................................39!
Alternative Smoothing Algorithm.............................................................40!
Defining Thresholds For Quantifying ‘No Biological Response’.............42!
‘Bulk’ Rapid Light Curve Characterization...............................................45!
Concentration Experiments........................................................................48!
Series of Algorithms Method Assessment.................................................49!
Discussion..............................................................................................................50!
Custom Algorithms vs WALZ...................................................................50!
Quantifying ‘No Biological Response’......................................................51!
Concentration Experiments........................................................................55!
Conclusions............................................................................................................55!
References..............................................................................................................57!
" SERIES OF CUSTOM ALGORITHMS CODE.........................................................60!
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LIST OF TABLES
PAGE
Table 1. Fluorescence Parameters (van Kooten and Snel 1990)...............................................8!
Table 2. Quenching Parameters (Maxwell and Johnson 2000, van Kooten and Snel
1990)..............................................................................................................................9!
Table 3. Non-Photochemical Quenching Parameters (Kropuenske et al.
2009,Maxwell and Johnson 2000).................................................................................9!
Table 4. Peak bins used in the 20 minute chart length for data processing in the series
of algorithms................................................................................................................26!
Table 5. Parameter calculation methods in the find_peaks_fnc.m algorithm..........................31!
Table 6. Results from the concentration experiments..............................................................49!
Table 7. Treatment viability assessment using established criterion and thresholds...............50!
Table 8. Summary of benefits using the series of algorithms compared to WALZ................56!
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LIST OF FIGURES
PAGE
Figure 1. Path of light in a photosynthetic cell, aka. light budget.............................................5!
Figure 2. Fluorescence trace with parameters (van Kooten and Snel 1990).............................7!
Figure 3. Comparison of variable fluorescence traces (Suggett et al. 2005): Multiple
turnover (A) and single turnover (B) flashes...............................................................11!
Figure 4. Programmed rapid light curve fluorescence trace with defined light sources:
Tetraselmis sp. culture.................................................................................................12!
Figure 5. Programmed rapid light curve fluorescence trace highlighting trends:
Tetraselmis sp. culture.................................................................................................13!
Figure 6. Programmed rapid light curve fluorescence trace showing Tetraselmis sp.
culture (A) compared to natural seawater samples (B)................................................20!
Figure 7. Flow Chart of Algorithms Written Using MATLAB...............................................21!
Figure 8. Example of algorithm combine_pcfcsv_.m creating variables from imported
pcf and csv files in MATLAB.....................................................................................23!
Figure 9. Example of an entire rapid light curve trace (A) and the magnified first peak
of that same rapid light curve (B)................................................................................25!
Figure 10. Example rapid light curve fluorescence traces for control, single dose
(half-dose), double dose treatment and Milli-Q water samples...................................27!
Figure 11. Examples of control and treatment samples before and after
smoothing/normalizing algorithm................................................................................28!
Figure 12. Ideal rapid light curve peak (A) compared to less than ideal peak (B)..................29!
Figure 13. Example of peak and corresponding parameter measurements using the
find_peaks_fnc.m algorithm.........................................................................................30!
Figure 14. Examples of a control (A) RLC plot and a treatment (B) RLC plot created
by the multicolorplot.m algorithm...............................................................................31!
Figure 15. Examples of control and treatment samples of the best-fit slope line
calculated for the actinic portion of the rapid light curve............................................32!
Figure 16. Illustration of SNR peak calculation for stats_.m algorithm..................................33!
Figure 17. Illustration of method for Calculation #1 designed to numerically
characterize the RLC trace for samples (control A, treatment B)................................34!
Figure 18. Examples of before and after step #1 for calculation #2 method...........................35!
Figure 19. RLC standard deviation calculation results for each sample type..........................35!
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Figure 20. Maximum fluorescence (F ) calculation results for each sample type..................36!
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Figure 21. Example of output from cyclecompare_.m comparing a control (A) and
treatment (B) RLC and peak 1 from the same control (C) and treatment (D)
RLC..............................................................................................................................37!
Figure 22. Example of the main features of the mkhtmlpg_.m output for an individual
RLC file analysis..........................................................................................................38!
Figure 23. Comparison of initial dark-adapted F /F calculation between the series of
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algorithms methods and WALZ...................................................................................40!
Figure 24. Mean F /F before and after the smooth_everything.m algorithm.........................41!
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Figure 25. The dark-adapted F /F ratio categorized by sample type.....................................42!
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Figure 26. Mean peak signal to noise ratio according to sample type (A) and
frequency of observed ratios (B) for the dark adapted peak #1...................................43!
Figure 27. Mean baseline slope according to sample type......................................................44!
Figure 28. Histogram of baseline slope values for control and treatment samples.................45!
Figure 29. Mean calculation #1 values across sample types...................................................46!
Figure 30. Histogram of RLC calculation #1 values for control and treatment samples.........46!
Figure 31. Mean calculation #1 values across sample types...................................................47!
Figure 32. Histogram of calculation #1 values for control and treatment samples.................47!
Figure 33. Initial F /F response to concentration methods....................................................48!
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Figure 34. Illustrated summary of the four criteria established for indicating viability..........49!
Figure 35. Results from a report by Welschmeyer and Maurer 2014 showing the
results of the side-by-side analysis of the MPN- and FDA-based methods for
quantifying live cells....................................................................................................54!
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ACKNOWLEDGEMENTS
I would like to express my deepest appreciation to my advisor Dr. Nicholas
Welschmeyer for his expert guidance and support as a student in his lab during the pursuit of
my degree. In addition, I would also like to give a special thanks to my other committee
members, Dr. Erika McPhee-Shaw for her wisdom, counsel and encouragement and to Dr. G.
Jason Smith for his thoughtful questions and comments throughout this process. To my
family and friends, thank you so much for your continued support during this journey.
Finally, I would like to thank the entire MLML community for making the time spent at
Moss Landing Marine Labs a memorable and enjoyable experience.