using muscle cell nuclear rna to improve condition measurements of
TRANSCRIPT
Using Muscle Cell Nuclear RNA to Improve Condition Measurements of Walleye Pollock (Theragra chalcogramma) Larvae Assessed With Flow Cytometry
Steven M Porter and Kevin M Bailey • Alaska Fisheries Science Center, NMFS, NOAA
Flow cytometry is a nucleic acid-based technique that uses cell cycle phase fractions to measure the condition of fish larvae. Most often a single tissue type is analyzed, and brain or muscle have been used in past studies. Cell-cycle information (fraction of nuclei in the S, and G2 phases), larval standard length, and temperature were covariates in a laboratory-developed model for measuring the condition of walleye pollock, Theragra chalcogramma, larvae using muscle cell nuclei (Porter and Bailey 2011). Here we show that an additional covariate based on nuclear RNA (nRNA) offers an improvement in classification accuracy for a similar type larval condition model by more clearly defining healthy and unhealthy condition.
Why Choose Nuclear RNA?Nuclear RNA is linked with the cell cycle because a specific G1 phase content may be required for progression
into the S phase. Also, it may react quicker to metabolic changes than cellular RNA making it more sensitive to
environmental variability and useful for assessing condition.
Flow CytometryMuscle cell nuclei from a single walleye pollock larva are stained with Invitrogen Syto RNASelect Green Fluorecent Cell stain (S32703, RNA stain) and DAPI (diamidino-2-phenylindole, DNA stain), and run through a flow cytometer. The resulting data file is analyzed using FCS Express (nuclear RNA) and Wincycle (DNA, cell cycle) flow cytometry software and results are used in the larval condition model.
RNA and DNA AnalysisLarvae were reared in fed (healthy) and unfed (unhealthy) feeding treatments. Plots of RNA and DNA fluorescence
showed that feeding larvae had a distinct group of aggregated S phase nuclei that joined the G1 and G2 phases
(Fig. 1), but for unfed larvae S phase nuclei were dispersed (Fig. 2). The proportion of G1 nuclei with the potential
to progress into the S phase (PropG1S) was the nRNA based covariate tested in the larval condition model. For
larvae that had a distinct S phase group, a subgroup of nuclei within the G1 phase was identified having RNA
fluorescence that ranged from the smallest S phase RNA fluorescence to the highest G1 phase value (Fig. 1,
G1B). Those nuclei had the potential to progress from G1 to the S phase. PropG1S was calculated by dividing the
number of G1B nuclei by the total number of G1 nuclei. The proportion for feeding larvae was significantly larger
than unfed larvae. PropG1S increased during the first two weeks of feeding for fed larvae, but for unfed
individuals it declined (Fig. 3). That pattern was not observed for nRNA fluorescence over the same time period
(Fig. 4), supporting the use of PropG1S as a covariate for assessing condition.
Condition Model Testing Model classification accuracy was compared between a quadratic discriminant analysis model that used larval
standard length, fraction of cells in the S phase, and fraction of cells in the G2 phase (Fig. 5 and 6; Control
model), and a Test model that added PropG1S to the Control model. Jackknifed cross-validation testing showed
that the Test model was 3% more accurate than the Control model. Furthermore, the classification accuracy of
unhealthy larvae increased by 6% when PropG1S was included. The overall classification accuracy of small
larvae (< 6.00 mm) improved 5% when PropG1S was included, but for larger larvae PropG1S had no effect on
classification accuracy.
ConclusionNuclear RNA improved classification accuracy of the larval condition model. This is significant because the S
and G2 phase fractions of small walleye pollock larvae can be highly variable due to first feeding and the overlap
of sizes of healthy and unhealthy larvae contributing to classification errors if using only those covariates.
Accurate assessment of the condition of walleye pollock larvae in the sea will improve understanding of
environmental processes affecting their survival, and this knowledge can enhance recruitment models used for
managing the fishery.
Healthy
RNA Fluorescence (arbitrary units)
DN
A Fl
uore
scen
ce (a
rbitr
ary
units
)
100 101 102 1038000
15250
22500
29750
37000
G1
G1B
S
G2
FCS Filename Gate # of Events X Geometric Mean
Y Geometric Mean
% of Gated Cells
% of All Cells
sp21oct11_03.fcs None 7277 24.0 18965.3 100.0 72.8sp21oct11_03.fcs G1 5088 21.3 16874.0 69.9 50.9sp21oct11_03.fcs G1B 2638 28.8 17199.3 36.3 26.4
Unhealthy
RNA Fluorescence (arbitrary units)
DN
A Fl
uore
scen
ce (a
rbitr
ary
units
)
100 101 102 1038000
14750
21500
28250
35000
G2
S
G1
DNA Content
Cel
l Num
ber
Healthy
0
60
120
180
240
300
360
0 64 128 192 256 320 384 448 512
CELL CYCLEDATA
Mean G1= 131.092CV G1 = 5.107% G1 = 73.435
Mean G2=263.348CV G2 = 4.775% G2 = 2.820
% S = 23.745
G2/G1 = 2.009%B.A.D.= 9.659% Agg. = 6.777Chi Sq.= 1.241Cell No.=9768% Debris= 18.622
G1
SG2
DNA Content
Cel
l Num
ber
Unhealthy
0
60
120
180
240
300
360
0 64 128 192 256 320 384 448 512
CELL CYCLEDATA
Mean G1= 101.405CV G1 = 4.649% G1 = 92.988
Mean G2=203.233CV G2 = 3.408% G2 = 6.429
% S = 0.583
G2/G1 = 2.004%B.A.D.= 17.506% Agg. = 6.243Chi Sq.= 0.866
Cell No.=6711% Debris= 29.573
G1
G2small S
RNA DNA
Control Model
Covariates: SL, arcsin , arcsin
Classification Treatment Healthy Unhealthy Percent Correct
Always-Fed (healthy)
49 14 78
Unfed (unhealthy) 6 44 88 overall correct 82
Test Model Covariates: SL, arcsin , arcsin , arcsin
Classification Treatment Healthy Unhealthy Percent Correct
Always-Fed (healthy) 49 14 78
Unfed (unhealthy) 3 47 94 overall correct 85
Larvae < 6.00 mm standard length
Classification Model Correct Incorrect Percent Correct Control 50 20 71
Test 53 17 76
Larvae ≥ 6.00 mm standard length
Classification Model Correct Incorrect Percent Correct Control 43 0 100
Test 43 0 100
ReferencePorter SM, Bailey KM (2011) Assessing the condition of walleye pollock Theragra chalcogramma (Pallas)
larvae using muscle-based flow cytometric cell cycle analysis. J Exp Mar Biol Ecol 399:101-109.
Figure 1. Figure 2.
Figure 3. Figure 4.
Figure 5. Figure 6.
The recommendations and general content presented in this poster do not necessarily represent the views or o�cial position of the Department of Commerce, the National Oceanic and Atmospheric Administration, or the National Marine Fisheries Service.
FCS Filename Gate # of Events X Geometric Mean
Y Geometric Mean
% of Gated Cells
% of All Cells
sp14jul11_01.fcs None 4887 28.9 13991.3 100.0 72.3
sp14jul11_01.fcs G1 4100 25.8 12829.7 83.9 60.7
s
This research was funded by North Pacific Research Board (project #926) and the Alaska
Fisheries Science Center.