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.