Imaging CA1 pyramidal cell ensembles recruited by stimulation of Schaffer collateral afferent inputs.

<p>A, Calcium transients in Oregon Green-1 loaded CA1 pyramidal cells are action-potential dependent. A₁, DIC image of the pyramidal cell layer. The pyramidal cell marked by a yellow asterisk was recorded in the loose patch configuration and SC inputs were evoked via a stimulating electrode in stratum radiatum. Stimulus strength was set at threshold for evoking spikes in the targeted cell. Scale bar, 20 µm. A₂, SC stimulation evokes calcium transients revealed by the ΔF/F image averaged across 6 stimulus trials. A_3, Average dF/F image of 4 trials in which a calcium transient was detected in the targeted cell (Successes). Traces of individual trials show loose patch recordings of action potentials from the targeted cell (top) and time course of the dF/F signal of the same cell. A₄, average dF/F image of 2 trials in which a calcium transient was not evoked (Failures). Traces indicate that the failure to evoke action potentials on single trials (top) did not generate calcium transients in the targeted cell. Calcium transients were always associated with spiking in all cells tested with loose patch recording (n = 6). B, Steps diagramming methods used to construct activity maps of cell ensembles. C, Activity maps of SC-evoked cell ensembles are stable over time. Left, Representative experiment illustrating cell ensembles recruited by SC stimulation at two time points (T1 and T2, 30 minute interval). Activated neurons in the pyramidal cell layer are color-coded blue and field EPSPs recorded in stratum radiatum during each imaging period are shown above. The activity maps and field EPSPs from the two periods are overlaid (T1 + T2, image color code: blue cells are recruited during both imaging periods, white cells are those recruited during T1 but absent during T2, red cells are those recruited during T2 but absent during T1). Scale bar for activity maps, 50 µm. Right, summary (n = 5) of the stability of cell ensembles over a 30 min time period.</p

Associative LTP of two independent Schaffer collateral pathways merges the ensembles of pyramidal cells recruited by the two pathways.

<p>A₁, Summary plot of fEPSPs showing associative LTP induced by simultaneous (paired) theta burst stimulation (TBS) of two SC pathways, while prior independent (unpaired) TBS does not cause potentiation (n = 4). Inset, recording configuration. A₂, fEPSPs and cell ensembles evoked by each pathway (red, green) in one experiment at the times indicated on the summary plot. Scale bar, 0.2 mV and 20 ms. B, Associative LTP significantly increases the overlap ratio (OLR) of the two SC ensembles. B₁, OLR was measured as the cells common between the two ensembles (SC₁₊₂) divided by the total cells in the two ensembles (SC₁ + SC₂ - SC₁₊₂, we subtract SC₁₊₂ in order not to count cells common to both ensembles twice). Summary data plot the increase in total cells (SC₁ +SC₂) and OLR of the two ensembles normalized to control conditions (n = 4 slices; **, p<0.01). B₂, Overlay of the two SC-evoked neuronal ensembles (red, green) shown in (A₂). Yellow cells indicate neurons common to the two ensembles. (C,D) Increasing afferent input by increasing stimulus strength expands the size of cell ensembles but associative LTP causes a greater increase in overlap between two SC ensembles. C, Associative LTP was induced by pairing a weak stimulus (one TBS, black arrow) in one pathway (black traces) with a strong stimulus (four TBS, gray arrow) to the other pathway (not shown). Cell ensembles were measured under control conditions (i), following an increase in stimulus strength (ii), when stimulus strength was returned back to control (iii) and following associative LTP (iv). D, Summary data showing change in total number of cells and OLR relative to control conditions for changes in stimulus strength and associative LTP (n = 3; *, p<0.05).</p

Timing-dependent associative synaptic plasticity enlarges active neural ensembles.

<p>A₁ Left, recording configuration. Right, induction protocol for studying timing-dependent plasticity. Example traces show the SC-evoked field EPSP followed 50 ms (top) or 5 ms (bottom) later by alveus stimulation (3 pulses, 100 Hz). A₂, Summary plot of timing-dependent LTP of SC fEPSPs induced by paired pre- and postsynaptic activity (n = 5). Single SC-evoked EPSPs (pre) were paired with brief trains of alveus stimulation (post, 3 pulses, 100 Hz) for 30 trials at 0.5 Hz. Pairing of alveus stimuli 50 ms following presynaptic activity (open triangle) had no effect on the fEPSP, while subsequent pairing using a 5 ms delay led to stable LTP. Top, representative fEPSPs recorded at the time points indicated on the summary plot. B, Pairing-induced LTP is NMDAR-dependent and enhances the number of pyramidal cells belonging to the SC ensemble. B₁, Pairing SC and alveus stimulation (5 ms delay) in the presence of D-APV (50 µM) has no effect on the fEPSP, while subsequent pairing following drug washout elicits LTP (n = 5). B₂, Pairing-induced LTP of fEPSPs is accompanied by an enlargement of the SC ensemble. Activity maps of SC-evoked CA1 cell ensembles from a representative experiment. Images and corresponding fEPSPs were acquired during the periods indicated by cameras in B₁. Activated neurons in the pyramidal cell layer are color-coded blue and cells added after pairing are colored red. Scale bar for activity maps, 50 µm. Scale bars for fEPSPs, 0.5 mV and 20 ms.</p

Bidirectional synaptic plasticity can merge neuronal ensembles without altering ensemble size.

<p>A₁, Summary plot of fEPSPs showing that low frequency stimulation (LFS, 300 pulses, 1 Hz) of two SC pathways (red, green) induces LTD and subsequent paired TBS induces LTP that returns the fEPSP to control conditions. A₂, Images and traces from one experiment collected at the time points indicated on the summary plot. LTD and LTP of fEPSPs were accompanied, respectively, by a reduction and a restoration of the size of neuronal ensembles recruited by the two SC pathways. Red and green represent the neuronal ensembles recruited by two independent SC pathways. Scale bars, 0.5 mV, 20 ms. B, Comparison of change in total number of cells and overlap between the two neuronal ensembles following LFS and subsequent paired TBS normalized to control conditions (n = 8; **, P<0.01). Schematics show the redistribution of the neuronal ensembles.</p

Pairing-induced synaptic plasticity selectively recruits cells from a defined population.

<p>A₁, Summary plot showing increases in fEPSPs following pairing-induced LTP and subsequent increase in SC stimulus strength (n = 6). Example fEPSPs (top traces) from one experiment at the indicated time points (scale bars, 0.2 mV, 20 ms). A₂, Cell activity maps from one experiment at the indicated time points (cameras, scale = 50 µm). Top row, Images show cells activated by the SC stimulation (blue) before (i) and after (i) pairing along with the new cells recruited (Cells added 1). Middle row, Cells activated following pairing (ii) and after increasing stimulus strength (iii) along with new cells recruited by the stimulus increase (Cells added II). Bottom row, images show cells activated by the alveus stimulation (orange) superimposed with those of the SC ensembles recruited by pairing-induced plasticity (Alv stim + I) and the increase in stimulus strength (Alv stim + II). Cells color-coded white belong to both the SC and alveus ensembles. B, Left, Summary showing that a larger fraction of newly added cells belong to the alveus population following LTP induction compared to those recruited by increased stimulation strength (n = 6; **, p<0.01). Right, diagram illustrating the dynamics of neuronal ensembles in this experiment. Blue and orange outlines represent the neuronal populations activated by SC and alveus stimulation, respectively. Hatched areas indicate cells that belong to both ensembles.</p

Multicolor Polymeric Nanoparticle Neuronal Tracers

Deciphering the targets of axonal projections plays a pivotal role in interpreting neuronal function and pathology. Neuronal tracers are indispensable tools for uncovering the functions and interactions between different subregions of the brain. However, the selection of commercially available neuronal tracers is limited, currently comprising small molecule dyes, viruses, and a handful of synthetic nanoparticles. Here, we describe a series of polymer-based nanoparticles capable of retrograde transport along neurons in vivo in mice. These polymeric nanoparticle neuronal tracers (NNTs) are prepared with a palette of fluorescent labels. The morphologies, charges, and optical properties of NNTs are characterized by analytical methods including fluorescence microscopy, electron microscopy, and dynamic light scattering. Cytotoxicity and cellular uptake were investigated to analyze cellular interactions in vitro. Regardless of the type of fluorophore used in labeling, each tracer was of similar morphology, size, and charge and was competent for retrograde transport in vivo. The platform provides a convenient, scalable synthetic approach for nonviral tracers labeled with a range of fluorophores for in vivo neuronal projection mapping

Examples of simulated responses to various visual stimuli.

(a) Spontaneous activity in the model was generated by waves of background excitation (“Bkg. on”, yellow arrow denotes the direction of motion of the yellow bar-like region) alternated with intervals of no background excitation (“Bkg. off”). (b) Model activity in a gray screen trial. Examples of membrane potential traces from simulated biophysical cells are in the middle. (c) Spike raster in response to a drifting grating (TF = 4 Hz, at 0 degrees direction). Bottom, example orientation tuning curves for an excitatory and inhibitory cell from simulation. (d) Spikes in response to a 50 ms full-field flash. (e) Spike raster for a single trial of a natural movie (top) and for a temporally scrambled version of the same movie (bottom). The raster in (b) shows all neurons and those in (c-e) for clarity show the 10,000 cells in the biophysical core of the model (inset on top of (c) zooms in on 200 cells). All rasters are examples from one trial; all trials used unique combinations of “Bkg. on” and “Bkg. off” states (shown at the bottom of plots), which overlapped (or not) in different ways with the visual stimuli.</p

LGN filters.

(a) Example responses of a single filter to visual stimuli, as a time-dependent firing rate that is the filter output (blue) and the firing rate computed from generated spike trains, averaged over all trials (green). The panel for images contains responses to 10 images shown in a sequence, 250 ms each. (b) F0 and F1 components of the responses to gratings for two example LGN filters. Tuning curves to orientation, SF, and TF are shown. The data points are averages from generated spike trains over time and over trials. (c) Connecting LGN filters to L4 cells. Geometry in the visual space is illustrated. The left panel shows centers of all filters present in a portion of the visual space around the mapped position of an example excitatory cell from L4. The dashed lines correspond to the “lasso” subfields around one illustrative L4 cell, used to capture input LGN filters of the ON, OFF, and ON/OFF type. The filters that are selected to send inputs to this L4 cells are in deep color; all other filters are dimmed. On the right, the same L4 cell with the filters selected to provide inputs to it are shown. For the filters, the approximate size of their receptive subfields is illustrated (a single subfield for ON or OFF filters and two subfields for ON/OFF filters; the radius of each RF circle is 2σC). (d) Convergence of LGN connectivity onto L4 cells that are not connected to each other (“No con.”), one-way connected (“One-way con.”), and reciprocally connected (“Reciprocal con.”). For each pair of L4 cells, the LGN convergence is defined as the number of LGN filters that connect to both cells divided by the sum of the numbers of LGN filters connected to each of the cells. The data are aggregated from three L4 models.</p

Assumed average numbers of synapses per an excitatory cell in L4 of mouse V1.

See text for details.</p

Mechanistic characterization of the model.

(a) Cortical amplification of the LGN inputs. The excitatory currents (from the LGN only, as well as total) in biophysical cells were measured using voltage clamp recordings. Top–an example; bottom–distributions of LGN contribution to the total excitatory current across excitatory and inhibitory cells (computed for each cell as the average current over time and over all trials of the preferred orientation). (b) Tuning curves for the mean and F1 component of the total and LGN-only currents, and their difference (“Sub”, i.e., the cortical component), as well as inhibitory current. The data for each cell were normalized to the peak value of the “Total” and shifted so that the preferred direction is at 0 degrees; averages and s.e.m. over all recorded excitatory cells are shown (TF = 2 Hz, contrast 80%). The inhibitory currents were normalized and aligned to their own peak values, since their magnitude is significantly higher than that of excitatory currents. (c) Amplification of excitatory current. Top, the total current vs. the LGN-only current, for an individual Rorb cell (each point is an average over time and over 10 trials). Linear fits (Itot = A ILGN + B) are shown for data aggregated from all grating directions, TFs, and contrasts (black), for one selected direction (yellow), and for a fixed contrast and TF (i.e., representing a sample direction tuning curve; right plot). Bottom, summary of linear fits across all cells analyzed. (d) Tuning curves for mean firing rate in full network simulations (“Full”, red) and in simulations where all connections except the feedforward connections from the LGN were removed (“LGN only”, blue). The data for each cell were normalized to the peak value of the “Full” and shifted so that the preferred direction is at 0 degrees; averages and s.e.m. over all excitatory cells are shown (TF = 2 Hz, contrast 80%). (e) Simulations of responses to a drifting grating, with the LGN activity switched off at 1000 ms. The black curve is the firing rate averaged over all cells, models, and trials; green is the exponential fit. (f) Distribution of the optogenetic modulation index (OMI) by cell type in responses to gratings, for simulations of optogenetic silencing of the Scnn1a population (top). Combined distribution for all biophysical excitatory cells is compared to the experimental result (bottom).</p