Effect of Liquid Handling Quality Control on Biological Assay Performance. Webinar sponsored by Artel Nathaniel Hentz, Ph.D.

Transcription

1 Effect of Liquid Handling Quality Control on Biological Assay Performance Webinar sponsored by Artel Nathaniel Hentz, Ph.D.

2 Outline Review accuracy and precision Discuss common liquid handler QC methods Artel MVS technology description Brief review of determining baseline liquid handler performance Discuss two case studies examining the effect of LH variability on assay performance: protein binding and enzyme activity 2

3 Accuracy vs. Precision Accuracy: The ability of a measurement to match the true value of the quantity being measured Often measured as % relative inaccuracy Precision: The ability of a measurement to be consistently repeatable Often measured as %CV or coefficient of variation Good Precision Bad Accuracy Bad Precision Bad Accuracy Bad Precision Good Accuracy Good Precision Good Accuracy 3

4 Review of Liquid Handler QC Methods and Their Relative Strengths 1 <1µL 1-20 µl >20 µl Precision Accuracy Multichannel Alternative fluids Gravimetric Absorbance (tartrazine) Fluorescence (fluorescein) Near Infrared 2 (977, 900 nm) Acoustic Dual-Dye ratiometric Strengths are based on a variety of factors including complexity, detection limits, whether a calibration curve is needed, etc. 2 Used by Molecular Devices and Biotek not necessarily for LH QC, but for pathlength correction to normalize well-to-well variability. 3 Acoustic is mentioned due to prevalence of acoustic dispensing, not because it is a widely accepted method of QC. 4

5 Artel MVS Technology Three Calculations 1. Blue dye (730 nm) is used to determine pathlength: l = A 730 /a b 2. Well liquid volume (V T ) is calculated using l and well dimensions and D 3. Red dye (520 nm) used to determine Sample volume (V S ) is calculated using: VVa a A A b Tr7520S30 5

6 Common Sources of Variability for LH Calibration Measurements Pipettor Reagents Tips Temperature Operator Plate type Mixing Detector These contribute directly to the liquid dispensation variability These affect the measurement process 6

7 Part 1: Experimental Outline Establish baseline performance of automated LH Identify best QC method Investigate plate types 96-well, clear-bottom plates from five different sources Minimize sources of variability Artel MVS reader is well characterized Artel MVS reagents are well characterized and NIST traceable Test LH to determine performance Investigate different volumes:10, 20 and 50 µl 7

8 Biotek Precision XS Description Manufacturer: Biotek (Winooski, VT) Single and 8-channel disposable tip dispenser Bulk reagent dispense Flexible compound reformatter 8

9 Precision XS: 10 L Dispense Disepense Volume ( L) Artel Greiner Costar Corning Nunc = 1.5 µl Artel Greiner Corning Costar Nunc Target Volume (µl) Number of data points Mean volume (µl) Relative Inaccuracy 3.20% 3.20% 7.80% 8.50% 3.80% Standard Deviation (µl) Coefficient of Variation 0.48% 1.07% 0.74% 1.11% 1.16% 9

10 Precision XS: 20 L Dispense 22.0 Disepense Volume ( L) = 1.5 µl 19.5 Artel Greiner Costar Corning Nunc Artel Greiner Corning Costar Nunc Target Volume (µl) Number of data points Mean volume (µl) Relative Inaccuracy 0.85% 0.80% 5.20% 5.60% 1.20% Standard Deviation (µl) Coefficient of Variation 0.20% 0.55% 0.52% 0.66% 0.44% 10

11 Precision XS: 50 L Dispense 54 Disepense Volume ( L) = 4 µl 48 Artel Greiner Costar Corning Nunc Artel Greiner Corning Costar Nunc Target Volume (µl) Number of data points Mean volume (µl) Relative Inaccuracy 0.02% -1.16% 3.2% 3.36% -1.30% Standard Deviation (µl) Coefficient of Variation 0.26% 0.34% 0.6% 0.58% 0.34% 11

12 Part I: Conclusions Plate type does make a difference when using the MVS system for LH verifications Artel Verification plates provide the best accuracy AND precision by design An automated LH can detect seemingly subtle differences in performance due to plate type Biotek Precision XS is capable of dispensing with better than 0.5% CV 12

13 Part II: Objectives Investigate effect of LH variability on assay performance Develop model high throughput assays 96-well format Protein binding and enzyme classes Validate and characterize Intentionally vary volume of each assay component by ~10% to determine effect on various inhibitor IC50s 13

14 Streptavidin:Biotin-Fl Assay Principle Streptavidin (SA) is a tetravalent biotin-binding protein that is isolated from Streptomyces avidinii and has a mass of 60.0 kda. SA has a very high affinity for biotin (K d = to -15 M). Fl Fl Fl Fl SA SA Biotin-Fluorescein (B-Fl) Quenched fluorescence Enhanced fluorescence 1. Waner, MJ; Mascotti DP. Journal of Biochemical and Biophysical Methods 70(6), 2008, A simple spectrophotometric streptavidin biotin binding assay utilizing biotin-4-fluorescein. 2. Ebner, A; Marek, M; Kaiser, K; Kada, G; Hahn, CD; Lackner, B; Gruber, HJ. Methods in Molecular Biology, 418, 2008, Application of biotin-4-fuorescein in homogeneous fluorescence assays for avidin, streptavidin, and biotin or biotin derivatives. 14

15 Streptavidin Assay Details Assay Components Phosphate buffered saline, ph 7.4, containing 0.1% BSA Black, non-binding 96-well plates Streptavidin (SA), 3 nm final Biotin-fluorescein (labeled ligand), 10 nm final Inhibitors, various nm Experimental Conditions Add 25 µl of Biotin-FL Add 25 µl of inhibitor Add 25 µl of SA Incubate for 60 min at room temperature Read fluorescence: Ex = 485 nm and Em = 515 nm Compare IC 50 s 15

16 Alternative Liquid Handlers The Precision XS was already decide upon and characterized Two additional liquid handlers were explored in order to mimic real assay development and assay transfer Finnpipette F2 manual 8-channel pipetter (Thermo Scientific) MicroFlo Select peristaltic bulk reagent dispenser (Biotek) 16

17 Assay Development: Manual Pipetting Manual Assay Performance Fluorescence Signal, RFU Min: 790 ± 12 (1.5%) Max: 8376 ± 428 (5.1%) Z-factor: n = 48 for Max and Min Well ID 17

18 Assay Development: Peristaltic Dispenser Min-Max 1 Min-Max Fluorescence Signal, RFU Z' = 0.75 Fluorescence Signal, RFU Z' = Well ID Well ID n = 96 for Max and Min 18

19 Assay Development: Precision XS Fluorescence Signal, RFU Automated LH Assay Performance Well ID Plate 1 Plate 2 Min: 826 ± 10 (1.2%) Max: 7633 ± 100 (1.3%) Z-factor: n = 48 for Max and Min Min: 819 ± 6 (0.8%) Max: 7543 ± 135 (1.8%) Z-factor: n = 48 for Max and Min 19

20 LH Variability Experimental Design Plate ID Biotin-Fl, L Cmpd, L SA, L n = 3 for each condition 20

21 Inhibitors Studied O Biotin HN H NH H Biotin Aminohexanoic Acid (Biotin-AH) HO O Desthiobiotin (DT-Biotin) Iminobiotin (I-Biotin) 21

22 Effect of LH Variability on Biotin Potency 9000 Fluorescence Signal (RFU) Log Inhibitor Concentration (nm) 22

23 Effect of LH Variability on Desthiobiotin Potency 9000 Fluorescence Signal (RFU) Log Inhibitor Concentration (nm) 23

24 Effect of LH Variability on Biotin Aminohexanoic Acid Potency 9000 Fluorescence Signal (RFU) Log Inhibitor Concentration (nm) 24

25 Effect of LH Variability on Iminobiotin Potency Log Inhibitor Concentration (nm) 25

26 Part II: Summary of Results Plate Control Statistics Inhibitor IC50, nm Plate ID Min Max S/B Z Biotin Biotin-AH DT-biotin I-Biotin potency

27 α-galactosidase Assay Principle 4-Methylumbelliferyl α- D-galactopyranoside HO HO HO O OH + OH Alpha-galactosidase is a lysosomal enzyme that catalyzes the hydrolysis of terminal α-galactosyl moieties from glycolipids and glycoproteins and is a target to address Fabry s disease. Product formation is detected by fluorescence: Ex 365 nm/em 448 nm 27 O Motabar et al., Current Chemical Genomics, 2010, 4, 67-73

28 α-galactosidase Assay Details Assay Components 50-mM citric acid, ph 4.5 Black, non-binding 96-well plates α-galactosidase (A-Gal), U/mL 4-MUG (fluorescent substrate), 100 µm Inhibitors, various nm Experimental Conditions Add 25 µl of 4-MUG Add 25 µl of inhibitor Add 25 µl of A-Gal Incubate for 40 min at room temperature Add 125 µl Stop solution Read fluorescence: Ex = 365 nm and Em = 448 nm Compare IC50s 28

29 Michaelis-Menten Enzyme Kinetics Galactosidase (0.001 U/mL) Vmax Km ph ph ph 5.9 ph 4.5 V i Substrate (um) 29

30 α-galactosidase Min-Max Study Signal (RFU) Well ID P1 max P1 min P2 max P2 min P3 max P3 min P4 max P4 min Z P1 = 0.92 Z P2 = 0.89 Z P3 = 0.94 Z P4 =

31 LH Variability Experimental Set-up Plate ID 4-MUG, L Cmpd, L A-Gal, L n = 3 for each condition 31

32 Effect of LH Variability on Potency: CMPD B

33 Effect of LH Variability on Potency: CMPD

34 Effect of LH Variability on Potency: CMPD

35 α-galactosidase Assay Results Summary Plate Control Statistics Inhibitor IC50, µm Plate ID Min Max S/B Z #9641 #B8299 # potency

36 Effect of Excess Reagent Addition Enzyme reactions typically need to be stopped: Does addition of stop reagent suppress the effect of LH variability on potency? Experiment: Perform experiment as described earlier; after 40-min incubation measure fluorescence before and after addition of 125 µl of Stop reagent; compare IC50s IC50 No Stop IC50 Stop P P P P P P P P MIN MAX MEAN Significance? 36

37 Part II: Conclusions LH variability does affect assay S/B is minimally affected Z-factor is not appreciably affected Compound potency is affected Higher potency inhibitors seem less affected by LH variability Resolution between similar potency compounds is decreased Lower potency inhibitors are more affected by LH variability Can lead to missing important chemical scaffolds 37

38 Future Directions Standardize liquid handlers to ease assay transfer Development automation HTS confirmation Confirmation LG/LO Replacement LHs Design of Experiments Identify critical reagent (most sensitive to variability) Allow data to match reagents to appropriate LH 38

39 Hypothetical HTS Scenario 4-component assay: enzyme, substrate, compounds and stop reagent Sensitivity to variability: (1) enzyme, (2) compound, (3) stop and (4) substrate You have 4 LHs: Peristaltic ±10% Pipette tip ±1% Valve ±3% Acoustic ±1% Substrate Enzyme Stop Compound 39

40 For more details, please see: Effect of Liquid-Handling Accuracy on Assay Performance (Journal of Laboratory Automation, first published on September12, 2013 as doi: / ) Effect on Liquid Handing Quality Control on Biological Assay Performance, poster presentation. THANK YOU 40

41 QUESTIONS? Contact: Nathaniel Hentz, PhD, North Carolina State University Tanya Knaide, Artel Or visit Artel: 41