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The functional diversity of retinal ganglion cells in the mouse

Nature volume 529, pages 345–350 (2016)Cite this article

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Abstract

In the vertebrate visual system, all output of the retina is carried by retinal ganglion cells. Each type encodes distinct visual features in parallel for transmission to the brain. How many such ‘output channels’ exist and what each encodes are areas of intense debate. In the mouse, anatomical estimates range from 15 to 20 channels, and only a handful are functionally understood. By combining two-photon calcium imaging to obtain dense retinal recordings and unsupervised clustering of the resulting sample of more than 11,000 cells, here we show that the mouse retina harbours substantially more than 30 functional output channels. These include all known and several new ganglion cell types, as verified by genetic and anatomical criteria. Therefore, information channels from the mouse eye to the mouse brain are considerably more diverse than shown thus far by anatomical studies, suggesting an encoding strategy resembling that used in state-of-the-art artificial vision systems.

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Figure 1: Data collection.
Figure 2: Functional RGC types of the mouse retina.
Figure 3: Classical alpha RGCs and their ‘mini’ counterparts.
Figure 4: Direction and orientation selectivity.
Figure 5: Mapping RGC groups to morphologies.

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Acknowledgements

We thank G. Eske for technical support and H. S. Seung, P. R. Martin, and A. S. Tolias for discussion. This work was supported by the Deutsche Forschungsgemeinschaft (DFG) (Werner Reichardt Centre for Integrative Neuroscience Tübingen, EXC 307 to M.B. and T.E.; BA 5283/1-1 to T.B.; BE 5601/1-1 to P.B.), the German Federal Ministry of Education and Research (BMBF) (BCCN Tübingen, FKZ 01GQ1002 to M.B. and T.E.), the BW-Stiftung (AZ 1.16101.09 to T.B.), the intramural fortüne program of the University of Tübingen (2125-0-0 to T.B.) and the National Institute of Neurological Disorders and Stroke of the National Institutes of Health (U01NS090562 to T.E.).

Author information

Author notes

  1. Tom Baden, Philipp Berens and Katrin Franke: These authors contributed equally to this work.

Authors and Affiliations

  1. Bernstein Centre for Computational Neuroscience, Tübingen, 72076, Germany

    Tom Baden, Philipp Berens, Katrin Franke, Miroslav Román Rosón, Matthias Bethge & Thomas Euler

  2. Centre for Integrative Neuroscience, University of Tübingen, Tübingen, 72076, Germany

    Tom Baden, Philipp Berens, Katrin Franke, Miroslav Román Rosón, Matthias Bethge & Thomas Euler

  3. Institute for Ophthalmic Research, Tübingen, 72076, Germany

    Tom Baden, Philipp Berens, Katrin Franke, Miroslav Román Rosón & Thomas Euler

  4. Baylor College of Medicine, Houston, 77030, Texas, USA

    Philipp Berens

  5. Institute of Theoretical Physics, University of Tübingen, Tübingen, 72076, Germany

    Philipp Berens & Matthias Bethge

  6. Graduate Training Centre of Neuroscience, University of Tübingen, Tübingen, 72074, Germany

    Katrin Franke & Miroslav Román Rosón

  7. Max Planck Institute of Biological Cybernetics, Tübingen, 72076, Germany

    Matthias Bethge

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Contributions

T.B., P.B., M.B. and T.E. designed the study; K.F. performed imaging experiments with help from T.B.; K.F. and M.R.R. performed electrophysiological experiments with help from T.B.; T.B., P.B., K.F. and M.R.R. performed pre-processing; P.B. developed the clustering framework with the help of M.B.; T.B. and P.B. analysed the data with input from T.E.; T.B., P.B. and T.E. wrote the manuscript.

Corresponding author

Correspondence to Thomas Euler.

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Competing interests

The authors declare no competing financial interests.

Additional information

Data (original data, clustering and grouping results) as well as Matlab code for visualization are available from http://www.retinal-functomics.org.

Extended data figures and tables

Extended Data Figure 1 Linking electrophysiology and imaging data (related to Fig. 1).

a, Simultaneously recorded RGC Ca2+ (top) and spiking (bottom) activity in response to binary spatial dense noise stimulation. b, Average Ca2+ event triggered by a single spike, averaged across n = 6 cells (shading indicates 1 s.d.); event decay was fitted (red) using a single exponential (for time constant τ, see inset, mean ± 1 s.d.) to yield an estimated impulse response. c, A linear prediction of Ca2+ (calculated by convolution of the impulse response with binarized spike traces) was compared to measured values to estimate the mean nonlinearity. d, Ca2+ (top) and spiking (bottom) response to the full-field chirp stimulus (Methods) simultaneously recorded in an RGC (red trace, Ca2+ signal predicted from spiking response). e, Number of scan fields as a function of blue/green index (BGi, see Methods) averaged over all ROIs in each field (Fig. 1a).

Extended Data Figure 2 Clustering and grouping (related to Fig. 2).

a–c, Selection of cluster size and cluster quality/consistency analysis. a, Normalized Bayesian information criterion (BIC) curves for non-DS (black) and DS (blue) cells. Arrows indicate the optimal numbers of clusters. b, Rank-ordered posterior probability curves indicating cluster quality. Curves were normalized for cluster size and averaged for non-DS (black) and DS (blue) clusters separately. Shaded area indicates 1 s.d. across clusters. c, Histogram of median correlation between the original clusters and clusters identified on 20 surrogate data sets, created by repeated subsampling of 90% of the original data set (bootstrapping); for each cluster, the best matching cluster from the original clustering was selected. d, Heat maps of Ca2+ responses to the four visual stimuli (see Fig. 1) of n = 11,210 cells from 50 retinas. Shown are raw data sorted by the response to the colour stimulus. Each line represents responses of a single cell with activity colour-coded such that warmer colours represent increased activity. e, Temporal features were extracted from the cells’ light responses (Methods) and used for automatic clustering (d to f). f, Heat maps showing clustered data (n = 72 clusters plus cells discarded based on signal-to-noise (S/N) ratio), with block height representing the number of included cells. g, Distributions of S/N (top) and GAD67 labelling (bottom) used to discard clusters and sort the remaining ones into retinal ganglion cells (RGCs), ‘uncertain’ RGCs and displaced amacrine cells (dACs). h, Heat maps showing n = 46 groups (divided into n = 32 RGC groups, including n = 4 ‘uncertain’ ones, and n = 14 dAC groups; sorted by response similarities) after re-clustering of large-soma cells (alpha cell post-processing, see panels i, j). i, Distribution of region of interest (ROI) area (as proxy for soma size) for all cells classified as RGCs and ‘uncertain’ (g). Inset, same distribution but on a log-scale. Dashed line marks threshold to separating large-soma cells (Methods). j, Results of re-clustering of large-soma cells (from i): heat maps show light-evoked Ca2+ responses to the four visual stimuli (see Fig. 1b). Clusters that resulted in new RGC groups are indicated; the remaining cells stayed with their original clusters.

Extended Data Figure 3 Group overview—functional groups classified as ‘uncertain’ RGCs and displaced amacrine cells (dACs) in the mouse retina (related to Fig. 2).

a, Clusters organized according to hierarchical trees (dendrograms, see Methods) and grouped based on functional similarity (see main text for details), resulting in n = 4 ‘uncertain’ RGCs (top) and n = 14 dAC groups (bottom). b, Mean Ca2+ responses to the four stimuli (see Fig. 1b) for each cluster. c, Histograms of selected properties, from left to right: ROI (soma) area, receptive field (RF) diameter (2 s.d. from Gaussian fit; see Fig. 1b and Extended Data Fig. 4), DS and OS indices (DSi and OSi, respectively, Methods). For details on each cluster, see also Supplementary Figures 1: 40–49 (‘uncertain’), and Supplementary Figures 1: 50–75 (dACs). d, Example experiment (left, from Fig. 1a); centre, dACs (lilac) and ‘uncertain’ RGCs (blue); right, colour-coded by broad categories, as in e. e, Total number of cells (top) and percentage of cells in sets of groups (bottom) per experiment (only experiments with ≥198 cells) illustrating consistency across experiments. Scale bar, 50 μm.

Extended Data Figure 4 Relationship between RGC receptive field centres and their dendritic arbors (related to Fig. 2).

a, b, Receptive field (RF) centre maps of a G8 transient OFF alpha RGC (a) and a G2 small-field RGC (b), with their reconstructed morphologies overlaid. 1- and 2-s.d. contours of RF centres fitted with 2D Gaussians are indicated by blue and red ovals, respectively. c, Area of RF centre fits from a, b as function of dendritic arbor area (n = 18 RGCs). Scale bars, 100 μm.

Extended Data Figure 5 Mapping RGC groups onto genetic types—functional diversity of PV- and Pcp2-positive RGCs (related to Fig. 2).

a, b, Diversity of PV-positive RGCs (red) in a PV:tdTomato mouse retina electroporated with OGB-1 (a, green). Ca2+ responses and receptive fields (b) from six PV-positive cells in exemplary field are shown (black, mean response, grey, single trials). The top four cells could be clearly matched to RGC groups (see Fig. 2), whereas the remaining two (x1, x2) were discarded due to the lack of responses to both full-field and moving bar stimuli; note, however that both cells yielded a clear RF. c, Ca2+ responses of functionally distinct PV-RGC groups (20 response types PVa–t, thereof 14 with n ≥ 3 cells). Traces colour-coded by group assignment (colours as in Fig. 2) represent mean responses, with individual cell responses in grey. d, Same for Pcp2-positive (six response types Pcp2a–f, thereof three with n ≥ 3 cells) RGC groups. e, Table illustrating the relationship between RGC groups (Fig. 2) and functional PV- and Pcp2-positive RGC types from (c, d). Numbers represent the total cell count of each allocation. Names in quotes (for example, “PV5”) refer to the cell’s original names (see PV (ref. 45) and Pcp2 studies (ref. 56)).

Extended Data Figure 6 Examples of RGC groups.

a–c, Functional ‘fingerprint’ of G10 RGCs, identified as local-edge-detector (W3) cells. Light-evoked Ca2+ responses of n = 149 cells: heat maps (top) illustrating individual responses, with response averages (with 1 s.d.) and firing rates estimated from Ca2+ signals (a; see Extended Data Fig. 1a–d) below. Ganglion cell layer (experiment from Fig. 1a) with G10 somata (green) and receptive fields (RFs, dotted) indicated (b). Grey circles mark cells with RFs that passed a quality criterion (Methods). Example morphology of a G10 cell filled after electrical single-cell recording (c). For a complete summary of the group’s properties, see Supplementary Figure 2: 10. d–f, Electrical single-cell recording of a G10 cell: spiking responses as raster plots and mean spike rates for chirp, moving bar and blue/green stimuli as well as time kernel derived from noise stimulus (d), polar plot of responses to moving bar (e) and RF map (f). g–i, G28a,b (n = 100) contrast-suppressed ON RGCs with sample morphology (i; G28a,b cell dye-injected after Ca2+ imaging). j–l, Electrical single-cell recording of a contrast-suppressed ON RGC with different morphology (l vs. i). m–r, G2 direction-selective OFF RGCs (n = 162) that stratify between the ChAT bands (o), as fingerprint (m, n) and exemplary electrical single-cell recording (p–r). Scale bars, 50 μm; grey lines in c, i, l, o, ChAT bands.

Extended Data Figure 7 Direction and orientation selectivity (related to Fig. 4).

a, Stimulus direction vs. time map for an exemplary direction-selective RGC with temporal (top) and directional (right) activation profiles shown; singular value decomposition (SVD) was used to estimate the time course and tuning function; individual stimulus repeats in grey, average in black. b, Reconstruction of direction vs. time map based on time course and tuning function of extracted by SVD. c, Statistical significance testing for direction selectivity (DS) or orientation selectivity (OS) was performed by projecting the direction/orientation profile on a single (for DS) or double (for OS) period cosine (blue) and the magnitude of the projection to the distribution of projections obtained by randomly permuting tuning angles from the original data (grey; bootstrapping). The P value is obtained by computing the percentile of the data (blue) in the bootstrap distribution (grey). d, e, P values for direction (d) and orientation (e) tuning as a function of the respective selectivity index (top, scatter plot; bottom, histogram; black, non-DS cells; light blue, DS cells; dark blue, OS cells). Note that tuning probability (pDS, pOS) only partially predicted tuning strength (DSi, OSi). f, Pairs of polar plots showing the distribution of preferred motion directions for all direction-selective (DS) RGCs together and for all DS RGC groups not shown in Fig. 4, (V, ventral; N, nasal direction; same group colour code as in Fig. 2). Top, plot of each pair: the cells’ individual preferred directions, with line length representing DSi and line grey level pDS (Methods). Bottom, plot of each pair: circular histogram of preferred direction. g, As for f but for orientation-selective (OS) RGCs. h–s, exemplary OS RGCs, illustrating the functional diversity within G17 (local ON trans. OS cells); none of them display strong full-field responses (h, l, p). A ‘vertically tuned’ ON OS cell (i, left) that shows little tuning to a dark moving bar (i, right; j, another example). Note the lobular structures bracketing the RF centre (coloured RF maps in k). m–o, Two examples for ‘horizontally tuned’ ON OS cells (m, n) with their respective RF maps (o). p–s, ON OS cell that shows weak tuning to bright moving bars (q) but strong OS to stationary bright and dark bars (r, left and right, respectively; Methods).

Extended Data Figure 8 Retinal distribution of PV-positive cells in the PVCre × Ai9tdTomato mouse line (related to Fig. 2).

a, b, Density map (a) and magnified sample areas (b) illustrate PV-labelling anisotropy.

Extended Data Figure 9 Mapping RGC groups to morphologies.

a–c, Exemplary morphologies of RGCs filled after electrical recording or Ca2+ imaging and subsequently clustered/sorted into specific RGC groups or discarded (c, right) based on their light-response S/N. Scale bars, 50 μm.

Extended Data Figure 10 RGC groups cover a basic feature space.

a, b, Relationship of four basic response indices of RGC groups. Disc area shows group size. Indices capture preference for stimulus polarity (ON–OFF index; Methods), for high vs. low temporal frequencies and contrasts (see below), as well as the full-field index (FFi; Methods), which reflects response preference for global (full-field chirp) versus local (moving bar) stimulation. Contrast and frequency indices represent contrasts of feature activation at respective time points during the full-field chirp stimulus, with j = 12, k = 9 for frequency, and j = 17, k = 15 for contrast. Before calculating ratios, feature activation (F) was normalized (0...1) by passing values through a cumulative normal distribution.

Supplementary information

Supplementary Information

This file contains a Supplementary Discussion, Supplementary tables 1-2 and Supplementary References. (PDF 542 kb)

Supplementary Figure 1

This Supplementary Figure contains a detailed summary of each cluster. Presentation similar to Extended Data Figure 6, but for each cluster and with more parameters plotted. a, light-evoked Ca2+ responses as heat maps (top) illustrating individual responses, with response median weighted by cluster-posterior below (for cell name, number of cells in group and coverage factor, CF, see bottom of each panel). If available and if nPV≥3, separate maps show responses from genetically labelled PV cells (cf. Extended Data Fig. 5) with their average response overlaid in brown. Black-filled traces: estimated spike-rates (a.u.) obtained by passing the mean Ca2+ response of a group through the reverse of the model shown in Extended Data Figure 1a-d. b,c, histograms quantifying the group’s properties, from left to right in (b): region-of-interest (ROI) area, soma volume, direction-selectivity index (DSi), orientation-selectivity index (OSi), receptive field (RF) centre diameter, and green-blue (G-B) chromatic preference; in (c): degree of glutamic acid decarboxylase (GAD67) immunoreactivity, full-field index (FFi), “chirp” quality index, DS quality index, and RF quality index (open bars, group data; shaded bars, data of all groups for comparison; brown bars, data for PV-positive cells in the group). d, percentage/number of transgenically identified (PV, Pcp2) and immunolabelled (SMI-32, melanopsin, ChAT) cells in this group; % labelled was calculated based only on cells that were recorded in the presence of each respective marker as indicated (e.g. only cells measured in the PV line could yield a negative PV label). (PDF 21379 kb)

Supplementary Figure 2

This Supplementary Figure contains a detailed summary of each group (presentation as for Supplementary Figure 1). (PDF 9510 kb)

Video 1: Ca2+ responses (background-subtracted fluorescence changes).

This video shows background-subtracted Ca2+ signals recorded in the ganglion cell layer (GCL) of a whole-mounted OGB-1 electroporated mouse retina using a two-photon microscope (montage of 9 consecutively recorded fields of 64 x 64 pixel @ 7.8 Hz; for details see Fig. 1a). Responses to moving bar (response averaged over 8 motion directions, looped 3 times) and full-field “chirp” stimulus (looped twice) are show. (MP4 17406 kb)

Video 2: Ca2+ responses colour-coded by retinal ganglion cell (RGC) group.

Same as Supplementary Video 1 but for mean responses of the RGC groups. (MP4 4084 kb)

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Baden, T., Berens, P., Franke, K. et al. The functional diversity of retinal ganglion cells in the mouse. Nature 529, 345–350 (2016). https://doi.org/10.1038/nature16468

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