diff --git a/bertopic/_bertopic.py b/bertopic/_bertopic.py
index 171136df..a2bd5119 100644
--- a/bertopic/_bertopic.py
+++ b/bertopic/_bertopic.py
@@ -1847,11 +1847,12 @@ def get_representative_docs(self, topic: int = None) -> List[str]:
         else:
             return self.representative_docs_
 
-    @staticmethod
     def get_topic_tree(
+        self,
         hier_topics: pd.DataFrame,
         max_distance: float = None,
         tight_layout: bool = False,
+        custom_labels: Union[bool, str] = False,
     ) -> str:
         """Extract the topic tree such that it can be printed.
 
@@ -1862,6 +1863,11 @@ def get_topic_tree(
                           based on the Distance column in `hier_topics`.
             tight_layout: Whether to use a tight layout (narrow width) for
                           easier readability if you have hundreds of topics.
+            custom_labels: If bool, whether to use custom topic labels that were defined using
+                           `topic_model.set_topic_labels`.
+                           If `str`, it uses labels from other aspects, e.g., "Aspect1".
+                           NOTE: Custom labels are only generated for the original
+                           un-merged topics.
 
         Returns:
             A tree that has the following structure when printed:
@@ -1897,9 +1903,40 @@ def get_topic_tree(
 
         max_original_topic = hier_topics.Parent_ID.astype(int).min() - 1
 
+        # Prepare tree labels to print
+        child_left_ids = hier_topics.Child_Left_ID.astype(int)
+        child_right_ids = hier_topics.Child_Right_ID.astype(int)
+
+        # Get the new parent labels generated from `hierarchical_topics`
+        new_left_labels = {int(row["Child_Left_ID"]): row["Child_Left_Name"] for idx, row in hier_topics.iterrows()}
+        new_right_labels = {int(row["Child_Right_ID"]): row["Child_Right_Name"] for idx, row in hier_topics.iterrows()}
+
+        if custom_labels:
+            left_labels = {}
+            if isinstance(custom_labels, str):
+                for topic, kws_info in self.topic_aspects_[custom_labels].items():
+                    label = "_".join([kw[0] for kw in kws_info[:5]])  # displaying top 5 kws
+                    left_labels[topic] = label
+            elif self.custom_labels_ is not None and custom_labels:
+                left_labels = {topic_id: label for topic_id, label in enumerate(self.custom_labels_, -self._outliers)}
+
+            right_labels = left_labels.copy()
+
+            # We want to preserve the original labels from `topic_aspects_` or `custom_labels_`
+            # while adding in those generated from `hierarchical_topics`
+            new_left_labels.update(left_labels)
+            new_right_labels.update(right_labels)
+
+            child_left_names = [new_left_labels[topic] for topic in child_left_ids]
+            child_right_names = [new_right_labels[topic] for topic in child_right_ids]
+
+        else:
+            child_left_names = hier_topics.Child_Left_Name
+            child_right_names = hier_topics.Child_Right_Name
+
         # Extract mapping from ID to name
-        topic_to_name = dict(zip(hier_topics.Child_Left_ID, hier_topics.Child_Left_Name))
-        topic_to_name.update(dict(zip(hier_topics.Child_Right_ID, hier_topics.Child_Right_Name)))
+        topic_to_name = dict(zip(child_left_ids.astype(str), child_left_names))
+        topic_to_name.update(dict(zip(child_right_ids.astype(str), child_right_names)))
         topic_to_name = {topic: name[:100] for topic, name in topic_to_name.items()}
 
         # Create tree