A field guide to every sensorgram shape we've seen across hundreds of labs in 15+ countries — what it means, and how to fix it.
Covers14 curve shapes
GroupsSetup · Assay · Operational
How to usePick what you see, read the fix
The 5 Phases of a Sensorgram (BASDR)
One binding cycle: baseline in buffer, the analyte arriving and binding, a steady state, the analyte leaving in buffer, then regeneration resets the surface.
Reading the cards: what you see what you would expect injection the problem
B
Baseline
Buffer flows over the sensor, establishing a stable reference signal.
A
Association
Analyte moves to the surface via mass transport and binds to the ligand.
S
Steady-State
Net rate of bound analyte is zero. Saturation may or may not be reached.
D
Dissociation
Buffer returns; analyte dissociates from the ligand.
R
Regeneration
Low pH buffer removes remaining bound analyte to restore the surface.
What are you seeing?
No exact match. Try another symptom, or contact us: we have likely seen it.
Part 1
Setup & Ligand Issues
Problems with ligand immobilization, regeneration, and sensor chip preparation.
SETUP
Insufficient Ligand Immobilization
No signal
What you see
Low or no RU response after analyte is flowed
Flat sensorgram with no binding signal
What to try
Increase ligand concentration
Increase coupling time
Change surface chemistry
SETUP
Incomplete Surface Regeneration
Baseline rising
What you see
Analyte not fully cleared between cycles
Progressive baseline increase across runs
Reduced binding capacity in subsequent cycles
What to try
Optimize regeneration buffer composition, pH, and ionic strength
Follow with original running buffer to re-establish baseline
Check for non-specific binding; increase flow rate and regeneration time
SETUP
Rebinding Effects
Slow dissociationPoor model fit
What you see
Analyte rebinds to nearby ligands after dissociation
Dissociation does not follow a single exponential
Baseline never recovered in dissociation phase
What to try
Decrease ligand density on sensor; increase flow rates
Use single-cycle kinetics
Inject soluble ligand as competitor during dissociation
SETUP
Noisy Data
Noisy data
What you see
High noise obscuring binding signal
Erratic RU fluctuations across the entire run
What to try
Increase analyte concentration
Increase amount of immobilized ligand on sensor
Part 2
Assay Issues
Issues arising from analyte preparation, buffer composition, and assay conditions.
ASSAY
Buffer Mismatch (Bulk Shift)
High signalPoor model fit
What you see
Square-shaped artifact in the binding curve
Large, rapid RU changes at start and end of injection
Analyte and running buffer compositions differ
What to try
Match analyte buffer components with running buffer
Dialyze or filter analyte into the same running buffer
Run buffer-only reference and subtract from analyte experiment
ASSAY
Carryover Between Cycles
Baseline risingSpikes / drops
What you see
Baseline increases across subsequent cycles
Sudden buffer jumps or spikes at the start of analyte injection
Often caused by high salt or sticky analyte
What to try
Verify complete regeneration between cycles
Reformulate analyte buffer
Add extra washing steps between injections
ASSAY
Analyte Aggregation
High signalNoisy dataSpikes / dropsPoor model fitSlow dissociation
What you see
Erratic, irregular or noisy binding curves
Sharp transient spikes or jumps in signal
Abnormally high RU and poor model fitting
What to try
Check protein folding and monodispersity
Reformulate analyte buffer; replenish with fresh soluble analyte
Filter or centrifuge to remove aggregates before injection
ASSAY
Mass Transport Limitations (MTL)
Poor model fitSlow dissociation
What you see
Slow, gradual RU increase during association — faster at higher flow rates
Analyte diffusion slower than binding rate
Poor data modeling at low analyte concentrations
What to try
Use higher flow rates and low-viscosity buffer
Decrease ligand density on the sensor
Use MTL-corrected models for data fitting
P4PRO with AffiPump is designed to minimize MTLs by delivering analyte quickly and evenly across the sensor surface.
ASSAY
Non-Specific Binding (NSB)
High signalPoor model fit
What you see
Steep RU increase from analyte interaction with sensor surface
High signal in reference channel — reflects diffusion, not binding
Poor model fitting
What to try
Increase salt concentration; adjust buffer pH
Add BSA or non-ionic surfactants (e.g., Tween 20)
Change ligand orientation or sensor surface chemistry
Part 3
Operational, Instrumentation & Other Issues
Issues arising from instrument state, fluidics, temperature, and compound effects.
OPERATIONAL
Baseline Drift
Baseline drifting
What you see
Gradual upward or downward RU drift during the run
May indicate temperature fluctuations, unstable sensor, or contamination
What to try
Stabilize temperature in the instrument environment
Replace sensor; equilibrate system with sufficient buffer
Flow running buffer until baseline is stable
OPERATIONAL
Air Bubble or Clog in Lines
Spikes / dropsNoisy data
What you see
Sudden, unpredictable RU spikes and drops during injection and dissociation
Erratic signal not correlated with binding events
What to try
Check instrument for fluidic leaks; purge lines
Degas samples before injection
Perform scheduled instrument maintenance
OPERATIONAL
Injection Failure
No signal
What you see
No RU response after analyte injection
May be caused by valve malfunction, air in syringe, or software error
What to try
Check injection valves and syringe for air
Restart software and re-run system maintenance
INSTRUMENTATION
Rounded Peak
Poor model fitSlow dissociation
What you see
Inefficient analyte dissociation from the ligand
Over- or under-estimation of binding affinity
Poor fit with standard 1:1 global model
What to try
Implement a regeneration step
Use a fit model other than 1:1 global fit
COMBINED
Combination Effects
High signalSpikes / dropsPoor model fitBaseline risingSlow dissociation
What you see
MTLs, protein aggregation, and buffer mismatch simultaneously present
Linear upward climb during association with no equilibrium plateau
Steady higher baseline; poor fitting across all models
What to try
Diagnose each effect individually before applying fixes
Address buffer mismatch first, then aggregation, then MTL