# Presentation: 576117

## Duplex Microsphere Immunoassay for Detection of IgM to WN and SLE VirusesFurther Developments and Validation  

## Objective of the MIA

**To replace the WN and SLE MAC-ELISAs with a single, equally sensitive, faster test.**
**WN and SLE viruses co-circulate in parts of the US and are routinely tested for concurrently.**

## Principle

- Microsphere-based immunologic assays (MIA’s) are similar to ELISAs, except instead of being attached to a plate, the assay components are attached to microspheres, and results are read using a modified flow cytometer.
- Similar problems to the ELISA are likely to arise.

## Slide 4

**Notes:**
Luminex dyes the beads by mixing a precise ratio of two fluorescent dyes, one red and the other infrared, in an organic solvent.  The beads swell in this environment allowing the dyes to be taken up.

## Slide 5

**Notes:**
The organic solvent is replaced with water and the unique ratios of dyes are trapped inside the beads. Millions of beads are dyed at the same time and the ratio of dyes identifies the bead set. Luminex uses 10 concentrations of each of the 2 dyes to give 100 different bead sets.

## All the bead sets can be mixed in a well or a tube. The instrument sorts the data based on the unique color ratios of the beads. The individual bead sets are tagged with biological tests where the instrument identifies the set number, and measures the binding results for the test.

**Notes:**
All 100 bead sets can be mixed in a single tube and the instrument can sort the data based on the unique color ratio of the beads. The individual bead sets can first be tagged with different biological tests where the instrument first identifies the bead set number, and also measures the binding results for the test.

## Slide 7

**Notes:**
In this example, antibody is bound to the surface of a bead set. When a sample is added, if any analyte is present it is captured by the antibody. A reporter antibody is added which carries a fluorescent molecule. The test that we developed varies from this example in that we add a capture antibody to the beads, which captures an antigen. The antigen binds with serum antibody and the antibody is detected by an anti-antibody fluorescent conjugate. We will go over this method in more detail shortly.

## Slide 8

**Notes:**
As these microspheres carrying the bioassays pass through the lasers single file the software picks up the reading associated with the 5.6 micron particle size. Any fluorescence associated with unbound material flowing through the detection chamber is not included in the result.

## A green laser quantifies the surface fluorescence, which represents the biological reaction. Simultaneously, the bead sets are classified by a red laser.

**Notes:**
A green laser quantifies the surface fluorescence, which represents the biological reaction. Simultaneously, the bead sets are classified by a red laser.

## WN/SLE Duplex MIA method

**Prior to the test the following are prepared:**
**All serum samples and controls are IgG depleted using protein G**
**Microsphere sets 32 and 57 are coupled to 6B6C-1 (commercially, stable >1 yr)**
**Viral and negative antigens are added to each of the coupled microspheres sets (stable for 1mo)**
**Test procedure:**
**WN/SLE viral antigens/coupled beads are concurrently added to 1 set of wells; negative antigens/coupled beads are added to another set of wells on a 96-well filter plate. **
**Plates are washed on vacuum manifold; serum (1:400) and anti-human IgM PE conjugate are added; mixed, and shaken for 1.5 hours.**
**Wells are filtered, washed, and beads resuspended. Plates are read on Luminex instrument. **
**Takes approx. 4.5 hours.**

## Typical plate format

**Typical plate format**

**WN pos control serum**

**SLE pos control serum**

**Neg control serum**

**Bead set 32/WN Ag**
**              ****+**
**Bead set 57/SLE Ag**

**Bead set 32/N Cos Ag**
**              ****+**
**Bead set 57/N SMB Ag**

**PG-treated test serum**

## SLE

- WN

- NEG

- Classification of 491 initial samples: 1. Plot log standardized WN(32) and SLE(57) values; 2. Superimpose PRNT result; 3. Apply QDA to classify. Subsequent samples are classified according to these lines.

## Slide 13

## Cross-validated QDA and MAC-ELISA classifications compared to the PRNT results of 491 samples that were used to generate the QDA classification rules.

|     PRNT |      QDA 
Classification |     % Correct
  QDA
 Class |  MAC-ELISA
 Classification |         % 
    Correct
 MAC-ELISA
      Class |
| --- | --- | --- | --- | --- |
| 
Result  # Samples | 
NEG  SLE  WN |  | 
NEG  SLE  WN |  |
| 
NEG      200

SLE         43

WN        248 | 
192    5      3

   3   40      0 

   4     0   244 | 
   96.0

   93.0

   98.4 | 
182    11      7

    0    32    11

    5      2   241 | 
       91.0

       74.4

       97.2 |

## 				Specificity

**	****	****	****	****	****WN    ****	****SLE   ****	****NS  ****	****NEG**
**LAC****	****	****	****12****	****	****0****	****0****	****0****	****12**
**Old flavi****	****	****	****10****	****	****0****	****0****	****0****	****10**
**DEN (IgM P/N<9)****	****	****15****	****	****0****	****0****	****1****	****14**
**DEN (IgM P/N****≥9)****	****	****18****	****	****1****	****3****	****5****	****9**
**YF vaccine****	****	****16****	****	****0****	****0****	****0****	****16**
**Other arbos****	****	****11****	****	****0****	****0****	****0****	****11**
**Syphilis****	****	****	****21****	****	****1****	****0****	****0****	****20**
**ANA****	****	****	****22****	****	****0****	****0****	****0****	****22**
**RF****	****	****	****13****	****	****0****	****0****	****0****	****10**
**LD (IgM)****	****	****	****10****	****	****0****	****0****	****0****	****10**
**LD (IgG)****	****	****	****10****	****	****0****	****0****	****0****	****10**
**NEG****	****	****	****154****	****	****0****	****0****	****6****	****148**
**TOTAL****	****	****	****312****	****	****2****	****3****	****13****	****294**

|  |
| --- |

**	****	****	****	****Specificity**

## Doing the math....

## Slide 17

## Development of MIA Classify Excel Add-in software to:

** ****Calculate the control values**
**Define constants relating to standardization of controls from all plates in development stage**
** ****Calculate adjusted test MFIs and normalizes them according to ratio of controls**
** ****Standardize adjusted values to historical values to allow all results to be directly compared**
**Set up variables and assign values based on the QDA**
**Calculate classification probabilities and produce a single result (WN, SLE or NEG)**
**Identify non-specific results**
**Order PRNT when necessary**

## K

- L

## Slide 20

## How well does the test work?

## Slide 22

## Validation

**Objectives:**
**To determine the correlation between the MAC-ELISA and the MIA.**
**To determine the reproducibility of the test between labs**
**To determine if any discrepant results were associated with particular runs**
**To determine whether nonspecific parameters can be refined and whether the 80% probability difference PRNT rule needs changing**

**Trained 4 states in the method in Sept 04.**
** ****Verified that the tests were working in each lab by use of a proficiency panel**
** ****Each lab tested +/-200 specimens by MIA, ELISA, and PRNT when necessary**
** ****Each sample was sent to CDC for comparative MIA testing**
** ****338 other samples submitted directly to CDC for WN/SLE testing were tested by MIA concurrently with other tests according to the CDC testing algorithm**
**Validation only for WN and NEG samples (insufficient SLE positives available for true validation)**

**Approach:**

## 1. MIA vs MAC-ELISA

**CDC MAC-ELISA**

**CDC**
**MIA**

| Class* | NEG | SLE | WN | NEG | % Agree |
| --- | --- | --- | --- | --- | --- |
| NEG | 145 | 3 | 0 | 148 | 98 |
| SLE | 0 | 8 | 0 | 8 | 100 |
| WN | 1 | 1 | 121 | 123 | 98 |
| Total | 146 | 12 | 121 | 279 |  |
| %
Agree | 99 | 67** | 100 |  |  |

- *  Nonspecific and equivocal results not included
- ** % agreement improves to 83% when compared to PRNT

## MIA vs MAC-ELISA: States

| Class* | NEG | WN | Total | % Agree |
| --- | --- | --- | --- | --- |
| NEG | 442 | 7 | 449 | 98 |
| WN | 25 | 249 | 274 | 91 |
| Total | 467 | 256 | 723 |  |
| % Agree | 95 | 97 |  |  |

- * SLE results not yet available for all states; Nonspecific and equivocal results not included.

**Combined**
**States**
**MIA**

**Combined States MAC-ELISA**

## WNV Agreement (log10 W comparison), State vs CDC

- State 1
- Intraclass Correlation Coefficient: 0.94;
-  (95% CI: 0.92, 0.95)
- State 2
- Intraclass Correlation Coefficient: 0.92;
- (95% CI: 0.89, 0.94)
- State 3
- Intraclass Correlation Coefficient: 0.92;
- (95% CI: 0.88, 0.94)
- State 4
- Intraclass Correlation Coefficient: 0.88;
- (95% CI: 0.84,0.91

## SLE Agreement (log10 S comparison), State vs CDC

_**SLE Agreement (log**__**10**__** S comparison), State vs CDC**_

- State 1
- Intraclass Correlation Coefficient: 0.75;
- (95% CI: 0.68, 0.80)
- State 2
- Intraclass Correlation Coefficient: 0.79;
- (95% CI: 0.73, 0.84)
- State 3
- Intraclass Correlation Coefficient: 0.94;
- (95% CI: 0.92, 0.95)
- State 4
- Intraclass Correlation Coefficient: 0.69;
- (95% CI: 0.62, 0.76)

## Plate distribution of results

## CDC MIA results for all 4 states showing nonspecific result distribution

## CDC MIA results for all 4 states

_**CDC MIA results for all 4 states**_
_** **__**showing <80% probability difference result distribution**_

## NONSPECIFICS

**Nonspecifics identified by lowest MFI/probabilities for true positive samples as seen in original data set**
**Total # samples in validation = 1136**
**Total samples classified as nonspecific to one or both antigens = 102**
**Of the 102 nonspecific samples 78 agreed with raw MIA interpretation. Greatest disparity seen among negative raw interpretations that confirm as positives. **
**Indicates that nonspecific criteria can be changed, especially with regards the WN raw interpretations, to reduce the number of PRNTs performed**
**SLE sample numbers are too small; therefore PRNTs may be necessary for all SLE MIA-positive samples for the time being.**

## 80% probability difference

**37 (3%) of all samples were recommended for PRNT due to a maximum probability difference between groups of <80%. **
**15/37 were also classified as nonspecific.**
**Of the specific samples (22) no discrepancies between the raw MIA interpretation and the final interpretation (PRNT/ELISA) were seen above 61%. **
**Conclude that the 80% probability difference could be lowered**

## Summary

** ****Data transformation algorithm and software developed**
** ****Temporary criteria to indicate PRNT confirmation determined**
** ****In-house and external test validation projects initiated**
** ****Test appears valid for WN and NEG samples; SLE not statistically validated due to low sample numbers**

## To do:

**Obtain more SLE samples if possible; in the meantime perform PRNTs on SLE positive samples**
**Add the validation samples to the original development data and recalculate the QDA; compare resulting classifications**
**Alter data transformation software accordingly**
**Adjust nonspecific criteria in the software and possibly lower the 80% cutoff to reduce the number of PRNTs recommended**

##         CDC

-         CDC
- Brad Biggerstaff
- Preeti Gupta
- Amanda Noga
- Olga Kosoy
- Janeen Laven
- Denise Martin

- States
- Ron Cheshier, AZ
- Giorgio Cosentino, CA
- Heather Masri, VA
- Rebecca Oesterle, MI
- Focus Technologies