diff --git a/docs/curate-df.ipynb b/docs/curate-df.ipynb
index efa1ef1bc..0913f59e1 100644
--- a/docs/curate-df.ipynb
+++ b/docs/curate-df.ipynb
@@ -86,7 +86,7 @@
    "id": "c7d74af9",
    "metadata": {},
    "source": [
-    "Define validation criteria and create a {class}`~lamindb.Curate` object."
+    "Define validation criteria and create a {class}`~lamindb.Curator` object."
    ]
   },
   {
@@ -109,7 +109,7 @@
     "}\n",
     "\n",
     "# pass validation criteria\n",
-    "curate = ln.Curate.from_df(df, categoricals=categoricals)"
+    "curate = ln.Curator.from_df(df, categoricals=categoricals)"
    ]
   },
   {
@@ -340,7 +340,7 @@
    },
    "outputs": [],
    "source": [
-    "curate = ln.Curate.from_anndata(\n",
+    "curate = ln.Curator.from_anndata(\n",
     "    adata, \n",
     "    var_index=bt.Gene.ensembl_gene_id,  # validate var.index against Gene.ensembl_gene_id\n",
     "    categoricals=categoricals, \n",
@@ -362,6 +362,24 @@
     "curate.validate()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "71a917d1",
+   "metadata": {},
+   "source": [
+    "Save the validated genes following the instruction:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e31dabcc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "curate.add_validated_from_var_index()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "614545ea",
@@ -421,7 +439,7 @@
    },
    "outputs": [],
    "source": [
-    "curate = ln.Curate.from_anndata(\n",
+    "curate = ln.Curator.from_anndata(\n",
     "    adata_validated, \n",
     "    var_index=bt.Gene.ensembl_gene_id,  # validate var.index against Gene.ensembl_gene_id\n",
     "    categoricals=categoricals, \n",
diff --git a/docs/introduction.ipynb b/docs/introduction.ipynb
index e45d05cc9..2a07cb6df 100644
--- a/docs/introduction.ipynb
+++ b/docs/introduction.ipynb
@@ -626,7 +626,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's use the high-level {class}`~lamindb.Curate` class to curate a `DataFrame`."
+    "Let's use the high-level {class}`~lamindb.Curator` class to curate a `DataFrame`."
    ]
   },
   {
@@ -639,8 +639,8 @@
    },
    "outputs": [],
    "source": [
-    "# construct a Curate object to validate & annotate a DataFrame\n",
-    "curate = ln.Curate.from_df(\n",
+    "# construct a Curator object to validate & annotate a DataFrame\n",
+    "curate = ln.Curator.from_df(\n",
     "    df_fixed_typo,\n",
     "    # define validation criteria\n",
     "    columns=ln.Feature.name,  # map column names\n",
@@ -712,7 +712,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Had we used `ln.Curate` from the beginning, we would have caught the typo."
+    "Had we used `ln.Cuartor` from the beginning, we would have caught the typo."
    ]
   },
   {
@@ -725,8 +725,8 @@
    },
    "outputs": [],
    "source": [
-    "# construct a Curate object to validate & annotate a DataFrame\n",
-    "curate = ln.Curate.from_df(\n",
+    "# construct a Curator object to validate & annotate a DataFrame\n",
+    "curate = ln.Curator.from_df(\n",
     "    df_with_typo,\n",
     "    # define validation criteria\n",
     "    columns=ln.Feature.name,  # map column names\n",
@@ -810,7 +810,7 @@
     "adata = ad.AnnData(df_fixed_typo[[\"CD8A\", \"CD4\", \"CD14\"]], obs=df_fixed_typo[[\"perturbation\"]])\n",
     "\n",
     "# create an annotation flow for an AnnData object\n",
-    "curate = ln.Curate.from_anndata(\n",
+    "curate = ln.Curator.from_anndata(\n",
     "    adata,\n",
     "    # define validation criteria\n",
     "    var_index=bt.Gene.symbol, # map .var.index onto Gene registry\n",
@@ -914,12 +914,13 @@
     "adata = ad.AnnData(df[[\"CD8A\", \"CD4\", \"CD38\"]], obs=df[[\"perturbation\"]])\n",
     "\n",
     "# validate, curate and save a new artifact\n",
-    "curate = ln.Curate.from_anndata(\n",
+    "curate = ln.Curator.from_anndata(\n",
     "    adata,\n",
     "    var_index=bt.Gene.symbol,\n",
     "    categoricals={adata.obs.perturbation.name: ln.ULabel.name},\n",
     "    organism=\"human\"\n",
     ")\n",
+    "curate.add_validated_from_var_index()\n",
     "curate.validate()\n",
     "artifact2 = curate.save_artifact(description=\"my RNA-seq dataset 2\")"
    ]
diff --git a/lamindb/_curate.py b/lamindb/_curate.py
index fefb04cfb..c73a84ac9 100644
--- a/lamindb/_curate.py
+++ b/lamindb/_curate.py
@@ -125,7 +125,7 @@ class DataFrameCurator(BaseCurator):
 
     Examples:
         >>> import bionty as bt
-        >>> curate = ln.Curate.from_df(
+        >>> curate = ln.Curator.from_df(
         ...     df,
         ...     categoricals={
         ...         "cell_type_ontology_id": bt.CellType.ontology_id,
@@ -208,6 +208,7 @@ def _save_columns(self, validated_only: bool = True, **kwargs) -> None:
             using_key=self._using_key,
             validated_only=False,
             source=self._sources.get("columns"),
+            exclude=self._exclude.get("columns"),
             **kwargs,
         )
 
@@ -223,6 +224,7 @@ def _save_columns(self, validated_only: bool = True, **kwargs) -> None:
                 validated_only=validated_only,
                 df=self._df,  # Get the Feature type from df
                 source=self._sources.get("columns"),
+                exclude=self._exclude.get("columns"),
                 warning=False,  # Do not warn about missing columns, just an info message
                 **kwargs,
             )
@@ -275,6 +277,7 @@ def _update_registry(self, categorical: str, validated_only: bool = True, **kwar
                 using_key=self._using_key,
                 validated_only=validated_only,
                 source=self._sources.get(categorical),
+                exclude=self._exclude.get(categorical),
                 **kwargs,
             )
 
@@ -356,7 +359,7 @@ class AnnDataCurator(DataFrameCurator):
 
     See also :class:`~lamindb.Curator`.
 
-    Note that if genes are removed from the AnnData object, the object should be recreated using :meth:`~lamindb.Curate.from_anndata`.
+    Note that if genes are removed from the AnnData object, the object should be recreated using :meth:`~lamindb.Curator.from_anndata`.
 
     See :doc:`docs:cellxgene-curate` for instructions on how to curate against a specific cellxgene schema version.
 
@@ -372,7 +375,7 @@ class AnnDataCurator(DataFrameCurator):
 
     Examples:
         >>> import bionty as bt
-        >>> curate = ln.Curate.from_anndata(
+        >>> curate = ln.Curator.from_anndata(
         ...     adata,
         ...     var_index=bt.Gene.ensembl_gene_id,
         ...     categoricals={
@@ -463,6 +466,7 @@ def _save_from_var_index(
             validated_only=validated_only,
             organism=organism,
             source=self._sources.get("var_index"),
+            exclude=self._exclude.get("var_index"),
         )
 
     def _update_registry_all(self, validated_only: bool = True, **kwargs):
@@ -565,7 +569,7 @@ class MuDataCurator:
     See also :class:`~lamindb.Curator`.
 
     Note that if genes or other measurements are removed from the MuData object,
-    the object should be recreated using :meth:`~lamindb.Curate.from_mudata`.
+    the object should be recreated using :meth:`~lamindb.Curator.from_mudata`.
 
     Args:
         mdata: The MuData object to curate.
@@ -582,7 +586,7 @@ class MuDataCurator:
 
     Examples:
         >>> import bionty as bt
-        >>> curate = ln.Curate.from_mudata(
+        >>> curate = ln.Curator.from_mudata(
         ...     mdata,
         ...     var_index={
         ...         "rna": bt.Gene.ensembl_gene_id,
@@ -667,6 +671,7 @@ def _save_from_var_index_modality(
             validated_only=validated_only,
             dtype="number",
             source=self._sources.get(modality, {}).get("var_index"),
+            exclude=self._exclude.get(modality, {}).get("var_index"),
             **kwargs,
         )
 
@@ -730,6 +735,7 @@ def add_new_from_columns(
             validated_only=False,
             df=self._mdata[modality].obs,
             source=self._sources.get(modality, {}).get("columns"),
+            exclude=self._exclude.get(modality, {}).get("columns"),
             **self._kwargs,  # type: ignore
             **kwargs,
         )
@@ -815,7 +821,8 @@ def validate(self, organism: str | None = None) -> bool:
                 field=var_field,
                 key=f"{modality}_var_index",
                 using_key=self._using_key,
-                exclude=self._exclude.get(f"{modality}_var_index"),
+                source=self._sources.get(modality, {}).get("var_index"),
+                exclude=self._exclude.get(modality, {}).get("var_index"),
                 **self._kwargs,  # type: ignore
             )
             validated_var &= is_validated_var
@@ -882,9 +889,9 @@ class Curator(BaseCurator):
 
     1. Instantiate `Curator` from one of the following dataset objects:
 
-    - :meth:`~lamindb.Curate.from_df`
-    - :meth:`~lamindb.Curate.from_anndata`
-    - :meth:`~lamindb.Curate.from_mudata`
+    - :meth:`~lamindb.Curator.from_df`
+    - :meth:`~lamindb.Curator.from_anndata`
+    - :meth:`~lamindb.Curator.from_mudata`
 
     During object creation, any passed categoricals found in the object will be saved.
 
@@ -1008,10 +1015,22 @@ def standardize_and_inspect(
     field: FieldAttr,
     registry: type[Record],
     standardize: bool = False,
+    exclude: str | list | None = None,
     **kwargs,
 ):
     """Standardize and inspect values using a registry."""
-    filter_kwargs = get_current_filter_kwargs(registry, kwargs)
+    # inspect exclude values in the default instance
+    values = list(values)
+    include_validated = []
+    if exclude is not None:
+        exclude = [exclude] if isinstance(exclude, str) else exclude
+        exclude = [i for i in exclude if i in values]
+        if len(exclude) > 0:
+            # exclude values are validated without source and organism
+            inspect_result_exclude = registry.inspect(exclude, field=field, mute=True)
+            # if exclude values are validated, remove them from the values
+            values = [i for i in values if i not in inspect_result_exclude.validated]
+            include_validated = inspect_result_exclude.validated
 
     if standardize:
         if hasattr(registry, "standardize") and hasattr(
@@ -1019,11 +1038,17 @@ def standardize_and_inspect(
             "synonyms",  # https://github.com/laminlabs/lamindb/issues/1685
         ):
             standardized_values = registry.standardize(
-                values, field=field, mute=True, **filter_kwargs
+                values, field=field, mute=True, **kwargs
             )
             values = standardized_values
 
-    return registry.inspect(values, field=field, mute=True, **filter_kwargs)
+    inspect_result = registry.inspect(values, field=field, mute=True, **kwargs)
+    inspect_result._validated += include_validated
+    inspect_result._non_validated = [
+        i for i in inspect_result.non_validated if i not in include_validated
+    ]
+
+    return inspect_result
 
 
 def check_registry_organism(registry: Record, organism: str | None = None) -> dict:
@@ -1075,35 +1100,32 @@ def _log_mapping_info():
         logger.indent = "   "
 
     registry = field.field.model
+
     kwargs = check_registry_organism(registry, organism)
     kwargs.update({"source": source} if source else {})
+    kwargs_current = get_current_filter_kwargs(registry, kwargs)
 
     # inspect the default instance
-    if exclude is not None:
-        exclude = [exclude] if isinstance(exclude, str) else exclude
-        # exclude values are validated without source and organism
-        inspect_result = registry.inspect(exclude, field=field, mute=True)
-        # if exclude values are validated, remove them from the values
-        values = [i for i in values if i not in inspect_result.validated]
-
     inspect_result = standardize_and_inspect(
         values=values,
         field=field,
         registry=registry,
         standardize=standardize,
-        **kwargs,
+        exclude=exclude,
+        **kwargs_current,
     )
     non_validated = inspect_result.non_validated
 
+    # inspect the using instance
     values_validated = []
     if using_key is not None and using_key != "default" and non_validated:
         registry_using = get_registry_instance(registry, using_key)
-        # inspect the using instance
         inspect_result = standardize_and_inspect(
             values=non_validated,
             field=field,
             registry=registry_using,
             standardize=standardize,
+            exclude=exclude,
             **kwargs,
         )
         non_validated = inspect_result.non_validated
@@ -1117,7 +1139,7 @@ def _log_mapping_info():
             public_records = registry.from_values(
                 non_validated,
                 field=field,
-                **get_current_filter_kwargs(registry, kwargs),
+                **kwargs_current,
             )
             values_validated += [getattr(r, field.field.name) for r in public_records]
         finally:
@@ -1137,9 +1159,13 @@ def _log_mapping_info():
     non_validated = [i for i in non_validated if i not in values_validated]
     n_non_validated = len(non_validated)
     if n_non_validated == 0:
-        logger.indent = ""
-        logger.success(f"{key} is validated against {colors.italic(model_field)}")
-        return True, []
+        if n_validated == 0:
+            logger.indent = ""
+            logger.success(f"{key} is validated against {colors.italic(model_field)}")
+            return True, []
+        else:
+            # validated values still need to be saved to the current instance
+            return False, []
     else:
         are = "are" if n_non_validated > 1 else "is"
         print_values = _print_values(non_validated)
@@ -1164,6 +1190,9 @@ def validate_categories_in_df(
     **kwargs,
 ) -> tuple[bool, dict]:
     """Validate categories in DataFrame columns using LaminDB registries."""
+    if not fields:
+        return True, {}
+
     if sources is None:
         sources = {}
     validated = True
@@ -1296,6 +1325,7 @@ def update_registry(
     source: Record | None = None,
     standardize: bool = True,
     warning: bool = True,
+    exclude: str | list | None = None,
     **kwargs,
 ) -> None:
     """Save features or labels records in the default instance from the using_key instance.
@@ -1355,7 +1385,8 @@ def update_registry(
             field=field,
             registry=registry,
             standardize=standardize,
-            **filter_kwargs,
+            exclude=exclude,
+            **filter_kwargs_current,
         )
         if not inspect_result_current.non_validated:
             all_labels = registry.from_values(
@@ -1374,6 +1405,7 @@ def update_registry(
             inspect_result_current.non_validated,
             field=field,
             using_key=using_key,
+            exclude=exclude,
             **filter_kwargs,
         )
 
@@ -1493,6 +1525,7 @@ def update_registry_from_using_instance(
     field: FieldAttr,
     using_key: str | None = None,
     standardize: bool = False,
+    exclude: str | list | None = None,
     **kwargs,
 ) -> tuple[list[str], list[str]]:
     """Save features or labels records from the using_key instance.
@@ -1518,6 +1551,7 @@ def update_registry_from_using_instance(
             field=field,
             registry=registry_using,
             standardize=standardize,
+            exclude=exclude,
             **kwargs,
         )
         labels_using = registry_using.filter(
diff --git a/pyproject.toml b/pyproject.toml
index 419ab91ad..e82152784 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -16,8 +16,8 @@ classifiers = [
 ]
 dependencies = [
     # Lamin PINNED packages
-    "lnschema_core==0.73.2",
-    "lamindb_setup==0.76.6",
+    "lnschema_core==0.73.3",
+    "lamindb_setup==0.76.7",
     "lamin_utils==0.13.4",
     "lamin_cli==0.16.2",
     # others
diff --git a/sub/lamindb-setup b/sub/lamindb-setup
index b782d20a6..9d9ce0d0b 160000
--- a/sub/lamindb-setup
+++ b/sub/lamindb-setup
@@ -1 +1 @@
-Subproject commit b782d20a620597d2444cfd062467d91775a69c41
+Subproject commit 9d9ce0d0b6e1c888b397be96603b72bb3219b0c6
diff --git a/sub/lnschema-core b/sub/lnschema-core
index 891b7c033..5c7240132 160000
--- a/sub/lnschema-core
+++ b/sub/lnschema-core
@@ -1 +1 @@
-Subproject commit 891b7c033ff3bf693c686006e31819cf7ff46a5b
+Subproject commit 5c72401326df22c45b5edac2f21e8a72d32ca99f
diff --git a/tests/core/test_curate.py b/tests/core/test_curate.py
index 79755c222..ba3bb8b95 100644
--- a/tests/core/test_curate.py
+++ b/tests/core/test_curate.py
@@ -86,7 +86,7 @@ def mock_transform():
 
 
 def test_df_annotator(df, categoricals):
-    curate = ln.Curate.from_df(df, categoricals=categoricals)
+    curate = ln.Curator.from_df(df, categoricals=categoricals)
     validated = curate.validate()
     assert validated is False
 
@@ -115,28 +115,28 @@ def test_custom_using_invalid_field_lookup(curate_lookup):
 
 def test_missing_columns(df):
     with pytest.raises(ValueError) as error:
-        ln.Curate.from_df(df, categoricals={"missing_column": "some_registry_field"})
+        ln.Curator.from_df(df, categoricals={"missing_column": "some_registry_field"})
     assert "Columns {'missing_column'} are not found in the data object!" in str(
         error.value
     )
 
 
 def test_additional_args_with_all_key(df, categoricals):
-    curate = ln.Curate.from_df(df, categoricals=categoricals)
+    curate = ln.Curator.from_df(df, categoricals=categoricals)
     with pytest.raises(ValueError) as error:
         curate.add_new_from("all", extra_arg="not_allowed")
     assert "Cannot pass additional arguments to 'all' key!" in str(error.value)
 
 
 def test_save_columns_not_defined_in_fields(df, categoricals):
-    curate = ln.Curate.from_df(df, categoricals=categoricals)
+    curate = ln.Curator.from_df(df, categoricals=categoricals)
     with pytest.raises(ValueError) as error:
         curate._update_registry("nonexistent")
     assert "Feature nonexistent is not part of the fields!" in str(error.value)
 
 
 def test_unvalidated_data_object(df, categoricals):
-    curate = ln.Curate.from_df(df, categoricals=categoricals)
+    curate = ln.Curator.from_df(df, categoricals=categoricals)
     with pytest.raises(ValidationError) as error:
         curate.save_artifact()
     assert "Data object is not validated" in str(error.value)
@@ -161,7 +161,7 @@ def test_clean_up_failed_runs():
 
     assert len(ln.Run.filter(transform=mock_transform).all()) == 2
 
-    curate = ln.Curate.from_df(pd.DataFrame())
+    curate = ln.Curator.from_df(pd.DataFrame())
     curate.clean_up_failed_runs()
 
     assert len(ln.Run.filter(transform=mock_transform).all()) == 1
@@ -172,13 +172,14 @@ def test_clean_up_failed_runs():
 
 
 def test_anndata_annotator(adata, categoricals):
-    curate = ln.Curate.from_anndata(
+    curate = ln.Curator.from_anndata(
         adata,
         categoricals=categoricals,
         var_index=bt.Gene.symbol,
         organism="human",
     )
     curate.add_validated_from("all")
+    curate.add_validated_from_var_index()
     curate.add_new_from("donor")
     validated = curate.validate()
     assert validated
@@ -192,9 +193,20 @@ def test_anndata_annotator(adata, categoricals):
     bt.CellType.filter().all().delete()
 
 
+def test_no_categoricals(adata):
+    curate = ln.Curate.from_anndata(
+        adata,
+        var_index=bt.Gene.symbol,
+        organism="human",
+    )
+    curate.add_validated_from("all")
+    validated = curate.validate()
+    assert validated
+
+
 def test_anndata_annotator_wrong_type(df, categoricals):
     with pytest.raises(ValueError) as error:
-        ln.Curate.from_anndata(
+        ln.Curator.from_anndata(
             df,
             categoricals=categoricals,
             var_index=bt.Gene.symbol,
@@ -204,7 +216,7 @@ def test_anndata_annotator_wrong_type(df, categoricals):
 
 
 def test_unvalidated_adata_object(adata, categoricals):
-    curate = ln.Curate.from_anndata(
+    curate = ln.Curator.from_anndata(
         adata,
         categoricals=categoricals,
         var_index=bt.Gene.symbol,
@@ -225,7 +237,7 @@ def test_mudata_annotator(mdata):
         "rna_2:donor": ln.ULabel.name,
     }
 
-    curate = ln.Curate.from_mudata(
+    curate = ln.Curator.from_mudata(
         mdata,
         categoricals=categoricals,
         var_index={"rna": bt.Gene.symbol, "rna_2": bt.Gene.symbol},