Holographic Characterization of Protein Aggregates

Transcription

1 Holographic Characterization of Protein Aggregates Size, morphology and differentiation one particle at a time (and fast) D. G. Grier, C. Wang, X. Zhong, & M. D. Ward New York University D. B. Ruffner & L. A. Philips Spheryx, Inc

2 Holographic Characterization Particle-resolved measurements of Radius: subvisible range 200 nm to 10 mm Refractive index: proxy for composition Useful for differentiation Morphology Useful for differentiation Useful for analyzing aggregation mechanism Concentration Divide observed counts by measured sample volume Real-time processes 30 measurements/second Complete analysis in 10 minutes 2

3 So, what s the problem? 3

4 Conventional Characterization (DLS) 40 PDMS spheres in water Malvern Zetasizer Nano ZS 4

5 Refractive Index Holographic Characterization ,000 spheres one sphere 0.5 Relative Probability Wang, et al., Soft Matter 11, 1062 (2015) Radius 0

6 Holographic Tracking & Characterization Lorenz-Mie Microscopy Tracking Characterization Parameters 3D Position Radius Refractive Index r p (t) a p n p Lee et al., Optics Express 15, (2007) 6

7 Holographic Tracking & Characterization How Lorenz-Mie Microscopy works Incident plane wave: Scattered field: : position : radius Focal plane : refractive index Lee et al., Optics Express 15, (2007) 7

8 Holographic Tracking & Characterization How Lorenz-Mie Microscopy works Lee et al., Optics Express 15, (2007) 8

9 Holographic Tracking & Characterization How Lorenz-Mie Microscopy works Lorenz-Mie scattering coefficients for a sphere: relative radius: refractive index: Lee et al., Optics Express 15, (2007) 9

10 Lorenz-Mie Microscopy Lee et al., Optics Express 15, (2007) 10

11 Holographic Tracking & Characterization Independently verified performance Property Precision Range 3D Position Δr p 3 nm μm 3 Radius Δa p 2 nm 200 nm 20 μm Refractive Index Δn p Concentration Δc p c p 10 3 ml ml 1 NIST Traceable Particles d p = 2 a p = 1.54 ± 0.05 μm n p = ± Bangs Labs #12035 Krishnatreya et al., Am. J. Phys. 82, 23 (2014) 11

12 Holographic Tracking & Characterization Independently verified performance Property Precision Range 3D Position Δr p 3 nm μm 3 Radius Δa p 2 nm 200 nm 20 μm Refractive Index Δn p Concentration Δc p c p 10 3 ml ml 1 Measured flow volume (no calibration) Krishnatreya et al., Am. J. Phys. 82, 23 (2014) 12 NIST traceable PS

13 Automated Holographic Characterization 1. Particles pass through laser beam in microfluidic channel 2. Microscope records interference pattern 3. Compare measurement to scattering theory 5. Build statistics: Each point characterizes one particle 4. Comparison yields : radius : refractive index : 3D position Size/refractive index distribution: for 2,500 spheres acquired in 10 minutes 13

14 Characterizing Colloidal Mixtures silica polystyrene Yevick, Hannel & Grier, Optics Express 22, (2014) 14

15 Characterizing Protein Aggregates BSA in Tris + added salt Wang et al., J. Pharm. Sci. 105, 1074 (2016) 15

16 Differentiating Silicone Oil Droplets Wang et al., J. Pharm. Sci. 105, 1074 (2016) 16

17 Characterizing Protein & Contaminants Wang et al., J. Pharm. Sci. 105, 1074 (2016) 17

18 Effective Medium Theory Porous particle Material: Medium: Refractive index: Volume fraction: Refractive index: Effective refractive index: Lorentz-Lorenz factor: Cheong et al., Soft Matter 7, 6816 (2011) 18

19 Fractal Aggregates Volume fraction: Effective Medium Theory: Size-Index Scaling: Wang et al., Soft Matter 12, 8774 (2016) 19

20 Fractal Scaling of Protein Aggregates Bovine Serum Albumin Bovine Insulin Filamentary clusters Cluster-cluster aggregates Wang et al., Soft Matter 12, 8774 (2016) 20

21 Holographic Characterization of Human IgG silicone 10 Large fractal dimension: compact clusters Differentiation between protein aggregates and silicone droplets Ruffner et al., unpublished (2016) 21

22 Holographic Characterization Detects, counts and characterizes subvisible protein aggregates in situ Differentiates by composition & morphology Fast, time-resolved measurements Independently verified precision & accuracy Minimal calibration 22

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24 Comparison with Industry Standard DLS (Malvern Zetasizer Nano) 1. Consistent mean radius results in common range 2. HVM also works for larger particles 3. HVM results have smaller (better) instrumental spread 4. HVM also yields refractive index (porosity) Cheong et al., Opt. Express 17, (2009) Cheong, Xiao, Pine & Grier, Soft Matter 7, 6816 (2011) 24

25 So, what do our clusters look like? hologram fit 25

26 So, what do our clusters look like? hologram fit Holographic Deconvolution Microscopy Dixon et al., Opt. Express 19, (2011) Chen et al., J. Pharm. Sci. 105, 1074 (2016) 26

27 So, what do our clusters look like? hologram fit 27

28 So, what do our clusters look like? hologram fit 28

29 So, what do our clusters look like? hologram fit 29