Surfaces & Practice

Designing an Experiment That Will Work

Every decision made before the first injection, and the reasoning behind each one.

22 min read 5 sections 12 sources cited (5 verified)

Start from the question, not the instrument#

The single biggest determinant of whether an SPR experiment succeeds is whether the person designing it knew which of four questions they were asking. The optimal surface for measuring kinetics is close to the worst possible surface for measuring concentration.

Question	Surface density	Flow rate	Concentrations	Timings
Kinetics (k_a, k_d)	Low — R_max 20–100 RU (Karlsson & Fält, 1997)	High, 30–100 µL min⁻¹	0.1–10 × K_D, geometric series	Enough curvature; long dissociation
Affinity at equilibrium	Low to medium	Moderate	0.1–10 × K_D	Long enough to plateau at the lowest concentration
Concentration (CFCA)	High, deliberately	Low, and at two values	Single unknown	Only the initial slope is used (Christensen, 1997)
Screening / ranking	Medium	Moderate	One or two, fixed	Short — throughput dominates

Four questions, four incompatible designs.

Buffers, in more detail than seems necessary#

Because SPR measures refractive index, buffer composition is not a background detail — it is part of the measurement. Two principles cover most of it.

The running buffer and the sample buffer must match

Any mismatch appears as a bulk shift: an instantaneous step at injection start and an equal step down at the end. Small mismatches are removed by referencing; large ones saturate the useful range and obscure the early association phase, which is where most of the kinetic information lives (Myszka, 1999). Dialyse or buffer-exchange the analyte into the actual running buffer — not into "the same recipe", which is not the same thing, because small pH and ionic strength differences between preparations are exactly what produces the mismatch.

Every component is there for a reason

Buffering species (HEPES, phosphate). 10 mM is typical. Enough to hold pH, low enough not to dominate refractive index.
Salt (~150 mM NaCl). Screens electrostatic non-specific binding. Reduce it and non-specific binding rises sharply; raise it and genuinely electrostatic interactions weaken (Ostuni et al., 2001).
EDTA. Chelates trace divalent metals. Omit it if your interaction requires Ca²⁺ or Mg²⁺ — a surprisingly frequent oversight with C-type lectins and integrins.
Surfactant (0.005% P20/Tween-20). Prevents protein adsorption to tubing and reduces air-bubble artefacts (Karlsson et al., 1994). Around the critical micelle concentration; more is not better.
Carrier protein or blocking agent. BSA or casein for difficult analytes. Note that these have their own refractive index contribution, so they must be in the running buffer too.
DMSO, for small molecules — see small molecules and fragments, where it needs its own correction procedure.

Regeneration, which is mostly about restraint#

You need to strip bound analyte without damaging the ligand. The temptation is to use whatever removes the analyte fastest, which is generally what also removes the most ligand.

Start gentle. 10 mM glycine-HCl pH 2.5 for 30 s is a reasonable first attempt for most protein–protein interactions (Karlsson et al., 1994).
Escalate only as needed. Lower pH, then higher salt (1–2 M NaCl or MgCl₂), then chaotropes, then combinations. Each escalation costs ligand.
Test it explicitly. Run the same analyte concentration five times with regeneration between. Plot maximum response against cycle number. Flat is good. A downward slope means you are destroying the surface, and every kinetic constant from that run is contaminated by a declining R_max.
Accept failure gracefully. If nothing regenerates without damage, switch to single-cycle kinetics (Karlsson et al., 2006) or to a capture format where regeneration acts on the capture interaction instead (Canziani et al., 2004).

The controls, and what each one rules out#

Control	Rules out
Reference flow cell (activated, deactivated, no ligand)	Bulk shift, drift, and matrix-level non-specific binding
Blank (buffer) injections through the whole series	Injection artefacts, valve spikes, systematic baseline behaviour (Myszka, 1999)
Repeat of one concentration at start and end	Surface decay and analyte degradation over the run
A non-binding analyte at the top concentration	Non-specific binding specific to the ligand surface
A ligand density series	Mass transport limitation and avidity (Myszka et al., 1998; Nieba et al., 1996)
Two flow rates at one concentration	Mass transport limitation, independently (Vijayendran et al., 1999)
A known reference interaction on the same chip	Instrument and surface health; anchors your panel for cross-lab comparison (Papalia et al., 2006)

The minimum control set. Each row exists to exclude a specific alternative explanation.

Run order and randomisation#

Almost every SPR run in the literature injects concentrations in ascending order. It is convenient, it minimises carryover, and it confounds concentration with time. If the surface is slowly decaying, a systematic decline over the run is perfectly correlated with concentration, and the fit will attribute it to the chemistry (Karlsson et al., 1994).

Randomise the concentration order where regeneration is reliable enough to permit it. This decorrelates concentration from time and converts a systematic bias into noise.
Bracket with replicates. If randomisation is impractical, at minimum repeat the same concentration at the beginning, middle and end.
Interleave the blanks rather than running them all at the start, so that the artefact you subtract is contemporaneous with the data you subtract it from.

None of this is exotic — it is ordinary experimental design, applied to an instrument whose ease of use tends to make people forget that it is an experiment.

Sources cited on this page

Listed alphabetically. Each badge records whether the bibliographic record was confirmed against Crossref. unverified marks a real, deliberately chosen source whose volume and page numbers we have not yet machine-checked — it is not a comment on the science.

Canziani et al., 2004G. A. Canziani, S. Klakamp, D. G. Myszka (2004). Kinetic screening of antibodies from crude hybridoma samples using Biacore. Analytical Biochemistry 325, 301–307. doi:10.1016/j.ab.2003.11.004 unverified
Ranking antibodies directly from crude supernatant, without purification.
Christensen, 1997L. L. H. Christensen (1997). Theoretical analysis of protein concentration determination using biosensor technology under conditions of partial mass transport limitation. Analytical Biochemistry 249, 153–164. doi:10.1006/abio.1997.2182 verified
The theoretical basis for using transport-limited initial rate as a concentration readout.
Karlsson et al., 1994R. Karlsson, H. Roos, L. Fägerstam, B. Persson (1994). Kinetic and concentration analysis using BIA technology. Methods 6, 99–110. doi:10.1006/meth.1994.1013 verified
Karlsson & Fält, 1997R. Karlsson, A. Fält (1997). Experimental design for kinetic analysis of protein–protein interactions with surface plasmon resonance biosensors. Journal of Immunological Methods 200, 121–133. doi:10.1016/S0022-1759(96)00195-0 verified
Where the low-density / high-flow-rate / analyte-range design rules come from.
Karlsson et al., 2006R. Karlsson, P. S. Katsamba, H. Nordin, E. Pol, D. G. Myszka (2006). Analyzing a kinetic titration series using affinity biosensors. Analytical Biochemistry 349, 136–147. doi:10.1016/j.ab.2005.09.034 unverified
Single-cycle kinetics: the whole concentration series in one injection sequence, no regeneration.
Myszka et al., 1998D. G. Myszka, X. He, M. Dembo, T. A. Morton, B. Goldstein (1998). Extending the range of rate constants available from BIACORE: interpreting mass transport-influenced binding data. Biophysical Journal 75, 583–594. doi:10.1016/S0006-3495(98)77549-6 verified
Shows transport can be fitted rather than merely avoided, and defines the transport coefficient kₜ.
Myszka, 1999D. G. Myszka (1999). Improving biosensor analysis. Journal of Molecular Recognition 12, 279–284. unverified
Origin of double referencing and blank-injection subtraction as standard practice.
Nieba et al., 1996L. Nieba, A. Krebber, A. Plückthun (1996). Competition BIAcore for measuring true affinities: large differences from values determined from binding kinetics. Analytical Biochemistry 234, 155–165. doi:10.1006/abio.1996.0067 unverified
A direct demonstration that surface-measured kinetic constants can diverge substantially from solution affinities, and a solution-competition format that avoids the problem.
Ostuni et al., 2001E. Ostuni, R. G. Chapman, R. E. Holmlin, S. Takayama, G. M. Whitesides (2001). A survey of structure–property relationships of surfaces that resist the adsorption of protein. Langmuir 17, 5605–5620. doi:10.1021/la010384m unverified
What makes a surface low-fouling, tested systematically across dozens of terminal chemistries.
Papalia et al., 2006G. A. Papalia, S. Leavitt, M. A. Bynum, et al. (2006). Comparative analysis of 10 small molecules binding to carbonic anhydrase II by different investigators using Biacore technology. Analytical Biochemistry 359, 94–105. unverified
A benchmark study in which many labs measured the same interactions; the spread between labs is the honest measure of SPR reproducibility.
Schuck & Minton, 1996P. Schuck, A. P. Minton (1996). Analysis of mass transport-limited binding kinetics in evanescent wave biosensors. Analytical Biochemistry 240, 262–272. doi:10.1006/abio.1996.0356 verified
The two-compartment model in the form most SPR software still implements.
Vijayendran et al., 1999R. A. Vijayendran, F. S. Ligler, D. E. Leckband (1999). A computational reaction–diffusion model for the analysis of transport-limited kinetics. Analytical Chemistry 71, 5405–5412. doi:10.1021/ac990672b unverified