Chemical synapses were detected manually, curve fit, and then characterized for strength, kinetics, and short-term plasticity in a multi-stage process. Manual connectivity calls were used to train a machine classifier (see Seeman, Campagnola, et al. 2018 for more detail). After training, the machine classifier revealed a set of potential false positives or false negatives that were manually re-checked . Electrical synapses were also detected and characterization is forthcoming.
Synapases were visually identified from average postsynaptic responses sorted by recording mode (voltage and current clamp) and membrane potential (see QC criteria for holding potential ranges for excitatory and inhibitory connections). Individual responses were time aligned to the presynaptic spike. Responses that failed QC were not included in the average.
|Users could visualize connections sorted by recording mode and membrane potential, then move the yellow line to set the onset of the response. The Fit Response button would then generate a fit constrained to ± 100 µs of the user-defined latency.|
The User Latency value was used to initialize automated curve fitting of the postsynaptic response. Parameters from the curve fits were shown for each quadrant. When the user was satisified with the curve fitting output, the goodness of fit was manually passed or failed. The user would take into consideration gap junctions, electrical artifacts, the shape of the fit or other factors when deciding whether the curve fit accurately reflected the response. Notes were also made by the user and used to update, and subsequently test, new fitting algorithms.
|In this example, the voltage clamp fits were failed because they could not accurately capture the shape of the response, while the current clamp response was a good fit and passed. No data was collected at depolarized potentials in current clamp and thus is ignored|
Once a chemical synapse was identified, the strength and kinetics of that synapse, as well as the short-term plasticity (STP), were characterized utilizing the curve fits generated during synapse detection.
The "strength" of a synapse is an important characteristic particularly when we want to start comparing synapses across cell class. However, we know that strength changes dynamically over time (as is highlighted in our analysis of STP) as well as stochastically from spike to spike. Given that our stimulus set utilizes trains of various frequencies we want to ensure that our metric of strength would be useful in comparisons across synapses and not be stimulus dependent. Our resting-state strength is a metric that works well for most synapses; however, some facilitating synapses have a very small resting-state strength which may be misleading. Rise and decay kinetics are more faithfully preserved even as the amplitude of the synapse changes and thus, was more straightforward to characterize.
Resting-state amplitude was determined from responses in which the presynaptic spike follows a period of quiescence. Usually this is the first pulse in each stimulus train. Individual responses were averaged and curve fit; the amplitude output of the fit served as our metric of strength.
|Average resting-state responses in current and voltage clamp. Each trace is the resting state average PSP/C from a single connection. Traces were fit and the fit amplitude is plotted above.|
Latency, rise and decay kinetics were also determined from curve fits to average synaptic responses. Kinetics were calculated for postynaptic currents (PSCs) and potentials (PSPs) held at either -70 or -55 mV depending on whether the synapse was excitatory or inhibitory (see QC requirement).
Short-term plasticity was analyzed from data recorded in current clamp utilizing different stimuli depedending on the analysis. We measured three main metrics of short-term plasticity: paired-pulse ratio (PPR), train-induced STP, and recovery from train-induced STP.
Short-term plasticity is often calculated as a ratio of response amplitudes for each pulse in the stimulus train. Many synapses in our dataset have small responses that are close to the noise level in our recordings making the use of an amplitude ratio unstable. This situation is accentuated for synapses that are strongly depressing or facilitating resulting in spurious ratio measurements. To avoid this instability, we use the 90th percentile PSP amplitude as an approximation of the "maximum" amplitude of a synapse and normalize our STP measurements by this value. Below are the calculations for each STP metric.
Paired-pulse STP was measured from the 50Hz stimulus as:
(Avg 2nd pulse - Avg 1st pulse) ÷ 90th percentile amplitude
Train-induced STP was measured from the 50 Hz stimulus as:
(Avg (6th, 7th, 8th pulses) - Avg 1st pulse) ÷ 90th percentile amplitude
Recovery from STP was measured from all stimulus frequencies with a 250 ms delay between the last induction pulse (8th) and first recovery pulse (9th):
(Avg (9th, 10th, 11th, 12th pulses) - Avg (1st, 2nd, 3rd, 4th pulses)) ÷ 90th percentile amplitude