diff --git a/src/spikeinterface/exporters/to_pynapple.py b/src/spikeinterface/exporters/to_pynapple.py
index 48eac36df6..a84cf59c04 100644
--- a/src/spikeinterface/exporters/to_pynapple.py
+++ b/src/spikeinterface/exporters/to_pynapple.py
@@ -8,6 +8,7 @@
 def to_pynapple_tsgroup(
     sorting_analyzer_or_sorting: SortingAnalyzer | BaseSorting,
     attach_unit_metadata=True,
+    attach_unit_properties=True,
     segment_index=None,
 ):
     """
@@ -21,6 +22,8 @@ def to_pynapple_tsgroup(
         If True, any relevant available metadata is attached to the TsGroup. Will attach
         `unit_locations`, `quality_metrics` and `template_metrics` if computed. If False,
         no metadata is included.
+    attach_unit_properties : bool, default: False
+        If True, attach properties of the sorting.
     segment_index : int | None, default: None
         The segment index. Can be None if mono-segment sorting.
 
@@ -59,14 +62,13 @@ def to_pynapple_tsgroup(
         for unit_id_int, unit_id in zip(unit_ids_ints, unit_ids)
     }
 
-    metadata_list = []
+    metadata_list = []  # init list to collect metadata dataframes
+
     if not unit_ids_castable:
         metadata_list.append(pd.DataFrame(unit_ids, columns=["unit_id"]))
 
     # Look for good metadata to add, if there is a sorting analyzer
     if attach_unit_metadata and isinstance(sorting_analyzer_or_sorting, SortingAnalyzer):
-
-        metadata_list = []
         if (unit_locations := sorting_analyzer_or_sorting.get_extension("unit_locations")) is not None:
             array_of_unit_locations = unit_locations.get_data()
             n_dims = np.shape(sorting_analyzer_or_sorting.get_extension("unit_locations").get_data())[1]
@@ -79,6 +81,16 @@ def to_pynapple_tsgroup(
         if (template_metrics := sorting_analyzer_or_sorting.get_extension("template_metrics")) is not None:
             metadata_list.append(template_metrics.get_data())
 
+    # attach unit properties from sorting
+    if attach_unit_properties:
+        property_df = pd.DataFrame(index=unit_ids)
+        property_keys = sorting.get_property_keys()  # get property keys of sorting
+        if len(property_keys):
+            for property_key in property_keys:  # loop through sorting's properties
+                property_data = sorting.get_property(property_key)
+                property_df[property_key] = list(property_data)
+            metadata_list.append(property_df)
+
     if len(metadata_list) > 0:
         metadata = pd.concat(metadata_list, axis=1)
         metadata.index = unit_ids_ints