DOI: 10.5061/dryad.qfttdz0v3
title: Charting the nanotopography of inner hair cell synapses using MINFLUX nanoscopy

description: For us to hear, the cochlea encodes sounds into neural signals at synapses
 of inner hair cells (IHCs) and the auditory nerve with remarkable
 fidelity. To achieve the high rates of temporally precise synaptic
 transmission over long periods of time, IHCs employ sophisticated
 ribbon-type active zones (AZ). In order for us to understand synaptic
 sound encoding, we need to decipher the underpinning molecular topography
 of these synapse which had remained challenging due to technological
 limitations. In our study, we applied 3-dimensional minimal flux optical
 nanoscopy (3D MINFLUX) to mouse IHC synapses to chart the position of key
 pre- and postsynaptic proteins with single digit nanometre resolution of
 imaging. We demonstrate that nanoclusters of channels and interacting
 proteins govern the topography of AZs and postsynaptic densities (PSDs).
 We count synaptic proteins, their nanoclusters and determine their spatial
 organization feeding into computational modelling of AZ function. In
 conclusion, our study reveals a nanocluster-based molecular AZ and PSD
 topography, likely serving as functional modules in synaptic sound
 encoding.
description: # Charting the nanotopography of inner hair cell synapses using MINFLUX
 nanoscopy Dataset DOI: [10.5061/dryad.qfttdz0v3](10.5061/dryad.qfttdz0v3)
 ## Description of the data and file structure This dataset was generated
 for the study *“Charting the nanotopography of inner hair cell synapses
 using MINFLUX nanoscopy”*, which aimed to elucidate the three-dimensional
 nanoscale organisation of key synaptic proteins at inner hair cell (IHC)
 ribbon synapses. Using MINFLUX nanoscopy, we acquired high-precision
 localisations to chart the organisation of key pre- and post-synaptic
 proteins, including RIBEYE, RBP2, CaV1.3, Piccolino, Homer1, GluA2, and
 VGLUT3 to generate a molecular atlas of these specialised sensory
 synapses. The data was processed to extract molecular coordinates, analyse
 spatial clustering patterns, and derive quantitative descriptors of
 synaptic architecture and nanocluster organisation. ### Files and
 variables #### File: Alpha_shape_volume.xlsx **Description:** Volume of
 alpha shape fits on coordinates of molecular counts for the proteins
 RIBEYE, RBP2, Homer1, CaV1.3, Piccolino and GluA2. Individual datapoints
 represent data from individual synapses for the respective protein
 analysed.  ##### Variables * **Protein**\ *Description:* Name of the
 synaptic protein analysed (RIBEYE, RBP2, Homer1, CaV1.3, Piccolino,
 GluA2).\ *Type:* Categorical * **Alpha_Shape_Volume**\ *Description:*
 Volume of the alpha shape fit generated from the 3D molecular coordinates
 of the given protein within a synapse.\ *Unit:* µm³\ *Type:* Numerical
 #### File: DCLF_Test_Summary.xlsx **Description:** Results of the
 Diggle-Cressie-Loosmore-Ford (DCLF) statistical test applied to MINFLUX
 data of different proteins across synapses. The DCLF test was used to
 assess whether molecular localisations exhibit statistically significant
 spatial clustering, based on deviation from complete spatial randomness
 over a range of distances (10 – 18 nm). Binary outcomes (TRUE/FALSE)
 indicate whether clustering was detected at a significance level of α =
 0.002. ##### Variables * **File**\ *Description:* Identifier for each
 synapse analysed containing protein name and numeric index.\ *Type:*
 Categorical * **DCLF True/False**\ *Description:* Outcome of the DCLF
 test.\ *Values:* * `TRUE`: Statistically significant clustering detected *
 `FALSE`: No significant clustering detected\ *Type:* Boolean #### File:
 List_of_epsilon_values_for_DBSCAN.xlsx **Description:** List of epsilon
 (ε) values used for DBSCAN clustering for individual MINFLUX dataset
 (individuals synapses for different proteins). The epsilon value defines
 the neighbourhood radius used for density-based clustering and was
 calculated as the square root of the sum of the squares of 2σ localisation
 precisions in each dimension (x, y, z). ##### Variables * **File**\
 *Description:* Identifier for each MINFLUX dataset, combining protein name
 and synapse index.\ *Type:* Categorical * **Epsilon (nm)**\ *Description:*
 Epsilon value (in nanometers) used as the neighbourhood radius parameter
 for DBSCAN clustering, computed from localisation precision.\ *Unit:*
 Nanometers (nm)\ *Type:* Numerical #### File: Molecular_counts.xlsx
 **Description:** Estimated molecular counts for each synapse imaged using
 3D MINFLUX for the proteins RIBEYE, RBP2, CaV1.3, Piccolino, Homer1, and
 GluA2. Molecular counts were derived from processed and filtered MINFLUX
 localizations (see Methods) and represent the number of molecular units
 detected per synapse for each protein. ##### Variables * **Protein**\
 *Description:* Name of the synaptic protein analysed (RIBEYE, RBP2,
 Homer1, CaV1.3, Piccolino, GluA2).\ *Type:* Categorical * **Molecular
 Count**\ *Description:* Number of molecular units detected in the synapse
 after data processing and filtering.\ *Type:* Integer\ *Unit:* Count
 (unitless) #### File: Nanocluster_analysis.xlsx
 **Description:** Nanocluster analysis results derived from molecular count
 coordinates of synaptic proteins. The analysis includes information on the
 number and organisation of nanoclusters, the spatial relationships between
 them, and intra- versus inter-cluster nearest neighbour distances. Data is
 presented across five worksheets. ##### **Variables (across sheets):** 1.
 **Sheet: Number of nanoclusters** * **File**: Identifier for the synapse
 or protein dataset * **Number of nanoclusters**: Total number of detected
 nanoclusters (unitless integer) 2. **Sheet: Number of unclustered units**
 * **File**: Dataset identifier * **Unclustered units**: Count of molecular
 units not assigned to any nanocluster (unitless integer) 3. **Sheet: Units
 per nanocluster** * **File**: Dataset identifier * **Units per cluster**:
 Number of molecular units per nanocluster (unitless integer) 4. **Sheet:
 Intercluster NND** * **File**: Dataset identifier * **Intercluster NND
 (nm)**: Nearest neighbour distances between nanocluster centroids *
 *Unit:* Nanometers (nm) 5. **Sheet: Intracluster NND** * **File**: Dataset
 identifier * **Intracluster NND (nm)**: Nearest neighbour distances
 between molecular units within a nanocluster * *Unit:* Nanometers (nm)
 #### File: Nearest_neighbour_distances.xlsx **Description:** Nearest
 neighbour distances derived from coordinates of molecular counts for the
 proteins RIBEYE, RBP2, Homer1, CaV1.3, Piccolino and GluA2. Each data
 point corresponds to a single unit and represents the Euclidean distance
 to its closest neighbouring unit within the same synapse. ##### Variables
 * **Protein**\ *Description:* Name of the synaptic protein analyzed
 (RIBEYE, RBP2, Homer1, CaV1.3, Piccolino, GluA2).\ *Type:* Categorical *
 **NND (nm)**\ *Description:* Nearest neighbour distance for a given
 molecular unit. Calculated in 3D from molecular count coordinates.\
 *Type:* Numerical\ *Unit:* Nanometers (nm) #### File: VGLUT3_SV_fits.xlsx
 **Description:** Estimated synaptic vesicle (SV) diameters obtained from
 2D MINFLUX localisations of VGLUT3. SV diameters were derived from
 circular fitting of clustered VGLUT3 localisations. Data from 4 MINFLUX
 images  ##### Variables * **VGLUT3 SV fits (vesicle diameter, nm)**\
 *Description:* Estimated diameter of individual SVs\ *Type:* Numerical\
 *Unit:* Nanometers (nm) #### File:
 2D_2colour_VGLUT3_RIBEYE_spectral_separated.zip **Description:** Contains
 2D MINFLUX localisation datasets of dual-color immunolabeled synapses
 stained for VGLUT3 and RIBEYE post spectral separation. Individual CSV
 files represent individual experiments and include localisation
 coordinates, photon counts, and metadata (refer to DCR values to
 distinguish between data from respective proteins). \ Refer to the
 description for file 'raw_efo_cfr_filtered_data.zip' for
 specifications of column entries containing MINFLUX parameters.  ####
 File: MINFLUX_Analysis_Python.zip **Description:** Contains Python script
 to process and analyse MINFLUX localisation data. Includes routines for
 preprocessing, clustering (DBSCAN), alpha shape volume estimation, nearest
 neighbour analysis, and quantification and visualisation of nanoclusters.
 Also includes data from an exemplary synapse for sample run and a readme
 file for description of analysis routine.  #### File:
 coordinates_units.zip **Description:** Contains coordinate files with
 molecular localisation data for various synaptic proteins imaged using 3D
 MINFLUX. Each file corresponds to an individual synapse for various
 synaptic proteins and includes final 3D coordinates (x, y, z) of molecular
 counts post filtering and DBSCAN.   #### File:
 raw_efo_cfr_filtered_data.zip **Description:** Raw 3D MINFLUX output files
 [filtered for EFO (Effective Frequency at Offset) and CFR (Centre
 Frequency Ratio) from PyMINFLUX and exported]. Each CSV file represents
 individual synapses for each protein condition, along with metadata. \ \
 **Key for columns specifying MINFLUX variables:** \ tid: Trace ID,
 consecutive localisations belonging to one localisation burst are assigned
 the same Trace ID\ tim: Time point of the localisation with respect to
 start of MINFLUX acquisition (s)\ x, y, z: coordinates of localisation in
 three dimensions (nm)\ efo: Effective Frequency at Offset (Hz) refers to
 the average frequency of the photons collected at the outer points of the
 MINFLUX pattern, corrected for background contributions, in contrast with
 EFC (Effective Frequency at Centre) which is measured at the center of the
 MINFLUX pattern.\ cfr: Centre Frequency Ratio, calculated as the ratio
 between Effective Frequency at Centre (EFC) and Effective Frequency at
 Offset (EFO), i.e. EFC/EFO. Maximum CFR thresholds can be used to filter
 out double events and poor-quality localisations.\ eco: Effective Counts
 at Offset are the background corrected photon counts at the MINFLUX
 scanning pattern except at the centre\ dcr: Detector Channel Ratio, 𝐷𝐶𝑅 =
 (𝑐𝑜𝑢𝑛𝑡𝑠~𝐶ℎ1,𝑜𝑓𝑓𝑠𝑒𝑡~)/(𝑐𝑜𝑢𝑛𝑡𝑠~𝐶ℎ1,𝑜𝑓𝑓𝑠𝑒𝑡~ + 𝑐𝑜𝑢𝑛𝑡𝑠~𝐶ℎ2,𝑜𝑓𝑓𝑠𝑒𝑡~). Only
 relevant for two-colour MINFLUX, when only one detector is in use DCR =
 1.\ dwell: Number of times the set dwell time in the MINFLUX sequence was
 repeated before the photon threshold was reached. Appropriate dwell time
 must be provided to pyMINFLUX upon loading a dataset.\ fluo:
 Fluorophore identifier, only relevant for two-colour MINFLUX when using
 pyMINFLUX’s DCR unmixing tool. ## Code/software Raw (EFO, CFR filtered)
 CSV files containing 3D MINFLUX data were generated using PyMINFLUX
 ([https://pyminflux.ethz.ch/](https://pyminflux.ethz.ch/); doi:10.5281/zenodo.7895501).  Subsequent processing of data was performed using a custom-written Python script, which has been provided along with the submission. The ZIP file contains a Python routine for clustering (DBSCAN), alpha shape volume estimation, nearest neighbour analysis, and quantification and visualisation of nanoclusters. Data from an exemplary synapse for sample run and a readme file for description of analysis routine have also been included. We encourage researchers to use the original MINFLUX data formats provided to ensure accurate and reproducible analyses. For support with software, workflows, or data handling, please contact the authors. ##