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Synaptic Physiology Methods: Quality Control

Ensuring the quality of the Synaptic Physiology Dataset is of critical importance. We applied quality control criteria at several stages of data processing and analysis. During the processing stages, in order for data to proceed into successive stages it had to pass quality control for the prior stage. During analysis of connection properties, each analysis (e.g., strength, kinetics, short-term plasticity) had independent quality control metrics. Below is a workflow of the quality control metrics required for inclusion at each stage of processing and analysis.

 

Data Processing

Data was first processed to ensure that it was of good enough quality to detect a connection

   

Criteria for inclusion

QC metric Voltage Clamp Current Clamp
Step 1: Evaluate the quality of a single stimulus trial. This is meant as a relatively inclusive criteria to ensure that the data is complete and cells are not dead    
Data to be evaluated: Postsynaptic recording of one stimulus trial
Holding current ±800 pA ±800 pA
Holding potential   -45 mV to -85 mV
Baseline noise < 200 pA < 5 mV

Step 2: Evaluate quality of individual pulse within a stimulus train. If data passed Step 1 we want to ensure that the response to each stimulus pulse within the train is of good quality

   
Data to be evaluated: Pre- and postsynaptic recording in response to one stimulus pulse
Presynaptic spike detected
Extra spikes None within ±8 ms None within ±8 ms

Standard deviation of postsynaptic response

This checks for a relatively stable recording

< 15 pA < 1.5 mV

Max postsynaptic amplitude

This checks for artifacts in the response

< 500 pA < 10 mV
Max postsynaptic potential   -40 mV
Step 3: Evaluate if the postsynaptic response meets criteria for detecting a connection.    
Data to be evaluated: postsynaptic recording in response to one presynaptic spike
Excitatory connection Membrane potential between -45 mV and -85 mV
Inhibitory connection Membrane potential between -45 mV and -60 mV
Step 4: If a connection was detected it was confirmed and postsynaptic responses were fit     

 

Data analysis

During characterization of a synapse, different QC criteria were applied based on the metric that was being analyzed.

Analysis QC requirement
Latency Average response curve fit matches user-passed fit
Kinetics Average response curve fit matches user-passed fit
Resting-state amplitdue Average response passes Step 3 for appropriate connection class
Short-term plasticity Average response passes Step 3 for appropriate connection class