diff --git a/.gitignore b/.gitignore
index 51c9dc49..20c38245 100644
--- a/.gitignore
+++ b/.gitignore
@@ -139,3 +139,5 @@ test_staging_dir/
 
 # dev config file
 dev_config.yaml
+
+.vscode/
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 2bd1d3b7..833463a4 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -198,13 +198,13 @@ These expectations are defined in the `/great_expectations/gx/plugins/expectatio
 
 #### Nested Columns
 
-If the transform includes nested columns (example: `druggability` column in `gene_info` tranform), please follow these steps:
-1. Add the nested column name to the `gx_nested_columns` flag in the configuration file for the specific transform. This will convert the column values to a JSON parsable string.
+If the transform includes nested columns (example: `druggability` column in `gene_info` tranform), please follow these four steps:
+1. In the config file, add the nested column name to the `gx_nested_columns` flag for the specific transform. This will convert the column values to a JSON parsable string.
 ```
 gx_nested_columns:
    - <nested_column_name>
 ```
-1. When creating the validator object in the gx_suite_definitions notebook, the nested column(s) must be included in the `nested_columns` list.
+2. When creating the validator object in the gx_suite_definitions notebook, the nested column(s) must be included in the `nested_columns` list.
 ```
 df = pd.read_json(<data_file>)
 nested_columns = ['<nested_column_name>']
@@ -212,11 +212,11 @@ df = GreatExpectationsRunner.convert_nested_columns_to_json(df, nested_columns)
 validator = context.sources.pandas_default.read_dataframe(df)
 validator.expectation_suite_name = "<suite_name>"
 ```
-1. When validating the value type of the nested column, make sure to specify it as a string (see Step 1 for reasoning):
+3. When validating the value type of the nested column, specify it as a string (see Step 1 for reasoning):
 ```
 validator.expect_column_values_to_be_of_type("<nested_column_name>", "str")
 ```
-1. A JSON file containing the expected schema must be added here: `src/agoradatatools/great_expectations/gx/json_schemas/<transform_name>/<column_name>.json`. Use the [JSON schema tool](https://jsonschema.net/app/schemas/0) to create the schema template for your nested column.
+4. A JSON file containing the expected schema must be added here: `src/agoradatatools/great_expectations/gx/json_schemas/<transform_name>/<column_name>.json`. Use the [JSON schema tool](https://jsonschema.net/app/schemas/0) to create the schema template for your nested column.
 
 ### DockerHub
 
diff --git a/gx_suite_definitions/biomarkers.ipynb b/gx_suite_definitions/biomarkers.ipynb
new file mode 100644
index 00000000..aec0b4f0
--- /dev/null
+++ b/gx_suite_definitions/biomarkers.ipynb
@@ -0,0 +1,269 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import synapseclient\n",
+    "\n",
+    "import great_expectations as gx\n",
+    "import pandas as pd\n",
+    "import json\n",
+    "\n",
+    "context = gx.get_context(project_root_dir='../src/agoradatatools/great_expectations')\n",
+    "\n",
+    "from agoradatatools.gx import GreatExpectationsRunner\n",
+    "from expectations.expect_column_values_to_have_list_length import ExpectColumnValuesToHaveListLength\n",
+    "from expectations.expect_column_values_to_have_list_members import ExpectColumnValuesToHaveListMembers\n",
+    "from expectations.expect_column_values_to_have_list_members_of_type import ExpectColumnValuesToHaveListMembersOfType"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Create Expectation Suite for Biomarkers Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "syn = synapseclient.Synapse()\n",
+    "syn.login()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "biomarkers_data_file = syn.get(\"syn63540269\").path"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Validator Object on Data File"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_json(biomarkers_data_file)\n",
+    "nested_columns = ['points']\n",
+    "df = GreatExpectationsRunner.convert_nested_columns_to_json(df, nested_columns)\n",
+    "validator = context.sources.pandas_default.read_dataframe(df)\n",
+    "validator.expectation_suite_name = \"biomarkers\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Add Expectations to Validator Object For Each Column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get the list of unique values for fields\n",
+    "with open(\"../src/agoradatatools/great_expectations/gx/json_schemas/immunohisto/biomarkers_unique_field_values.json\", \"r\") as file:\n",
+    "    unique_field_values = json.load(file)\n",
+    "print(unique_field_values)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# points\n",
+    "validator.expect_column_values_to_be_of_type(\"points\", \"str\")\n",
+    "with open(\"../src/agoradatatools/great_expectations/gx/json_schemas/immunohisto/points.json\", \"r\") as file:\n",
+    "    points_schema = json.load(file)\n",
+    "validator.expect_column_values_to_match_json_schema(\"points\", json_schema=points_schema)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# model\n",
+    "validator.expect_column_values_to_be_of_type(\"model\", \"str\")\n",
+    "validator.expect_column_values_to_not_be_null(\"model\")\n",
+    "\n",
+    "# List of accepted values\n",
+    "field_name = \"model\"\n",
+    "if field_name in unique_field_values:\n",
+    "    validator.expect_column_distinct_values_to_be_in_set(field_name, unique_field_values[field_name])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# type\n",
+    "validator.expect_column_values_to_be_of_type(\"type\", \"str\")\n",
+    "validator.expect_column_values_to_not_be_null(\"type\")\n",
+    "\n",
+    "# List of accepted values\n",
+    "field_name = \"type\"\n",
+    "if field_name in unique_field_values:\n",
+    "    validator.expect_column_distinct_values_to_be_in_set(field_name, unique_field_values[field_name])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# units\n",
+    "validator.expect_column_values_to_be_of_type(\"units\", \"str\")\n",
+    "validator.expect_column_values_to_not_be_null(\"units\")\n",
+    "\n",
+    "# List of accepted values\n",
+    "field_name = \"units\"\n",
+    "if field_name in unique_field_values:\n",
+    "    validator.expect_column_distinct_values_to_be_in_set(field_name, unique_field_values[field_name])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# age_death\n",
+    "validator.expect_column_values_to_be_of_type(\"age_death\", \"int\")\n",
+    "validator.expect_column_values_to_not_be_null(\"age_death\")\n",
+    "validator.expect_column_values_to_be_between(\"age_death\", strict_min_value=0, max_value=100)\n",
+    "\n",
+    "# List of accepted values\n",
+    "field_name = \"age_death\"\n",
+    "if field_name in unique_field_values:\n",
+    "    validator.expect_column_distinct_values_to_be_in_set(field_name, unique_field_values[field_name])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# tissue\n",
+    "validator.expect_column_values_to_be_of_type(\"tissue\", \"str\")\n",
+    "validator.expect_column_values_to_not_be_null(\"tissue\")\n",
+    "\n",
+    "# List of accepted values\n",
+    "field_name = \"tissue\"\n",
+    "if field_name in unique_field_values:\n",
+    "    validator.expect_column_distinct_values_to_be_in_set(field_name, unique_field_values[field_name])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# unique entries ExpectSelectColumnValuesToBeUniqueWithinRecord\n",
+    "validator.expect_select_column_values_to_be_unique_within_record(column_list=[\"model\", \"type\", \"age_death\", \"tissue\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "validator.save_expectation_suite(discard_failed_expectations=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Checkpoint and View Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "checkpoint = context.add_or_update_checkpoint(\n",
+    "    name=\"agora-test-checkpoint\",\n",
+    "    validator=validator,\n",
+    ")\n",
+    "checkpoint_result = checkpoint.run()\n",
+    "context.view_validation_result(checkpoint_result)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Build Data Docs - Click on Expectation Suite to View All Expectations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "context.build_data_docs()\n",
+    "context.open_data_docs()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/gx_suite_definitions/pathology.ipynb b/gx_suite_definitions/pathology.ipynb
new file mode 100644
index 00000000..17583b56
--- /dev/null
+++ b/gx_suite_definitions/pathology.ipynb
@@ -0,0 +1,268 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import synapseclient\n",
+    "\n",
+    "import great_expectations as gx\n",
+    "import pandas as pd\n",
+    "import json\n",
+    "\n",
+    "context = gx.get_context(project_root_dir='../src/agoradatatools/great_expectations')\n",
+    "\n",
+    "from agoradatatools.gx import GreatExpectationsRunner\n",
+    "from expectations.expect_column_values_to_have_list_length import ExpectColumnValuesToHaveListLength\n",
+    "from expectations.expect_column_values_to_have_list_members import ExpectColumnValuesToHaveListMembers\n",
+    "from expectations.expect_column_values_to_have_list_members_of_type import ExpectColumnValuesToHaveListMembersOfType"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Create Expectation Suite for Pathology Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "syn = synapseclient.Synapse()\n",
+    "syn.login()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pathology_data_file = syn.get(\"syn63644533\").path"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Validator Object on Data File"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_json(pathology_data_file)\n",
+    "nested_columns = ['points']\n",
+    "df = GreatExpectationsRunner.convert_nested_columns_to_json(df, nested_columns)\n",
+    "validator = context.sources.pandas_default.read_dataframe(df)\n",
+    "validator.expectation_suite_name = \"pathology\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Add Expectations to Validator Object For Each Column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get the list of unique values for fields\n",
+    "with open(\"../src/agoradatatools/great_expectations/gx/json_schemas/immunohisto/pathology_unique_field_values.json\", \"r\") as file:\n",
+    "    unique_field_values = json.load(file)\n",
+    "print(unique_field_values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# points\n",
+    "validator.expect_column_values_to_be_of_type(\"points\", \"str\")\n",
+    "with open(\"../src/agoradatatools/great_expectations/gx/json_schemas/immunohisto/points.json\", \"r\") as file:\n",
+    "    points_schema = json.load(file)\n",
+    "validator.expect_column_values_to_match_json_schema(\"points\", json_schema=points_schema)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# model\n",
+    "validator.expect_column_values_to_be_of_type(\"model\", \"str\")\n",
+    "validator.expect_column_values_to_not_be_null(\"model\")\n",
+    "\n",
+    "# List of accepted values\n",
+    "field_name = \"model\"\n",
+    "if field_name in unique_field_values:\n",
+    "    validator.expect_column_distinct_values_to_be_in_set(field_name, unique_field_values[field_name])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# type\n",
+    "validator.expect_column_values_to_be_of_type(\"type\", \"str\")\n",
+    "validator.expect_column_values_to_not_be_null(\"type\")\n",
+    "\n",
+    "# List of accepted values\n",
+    "field_name = \"type\"\n",
+    "if field_name in unique_field_values:\n",
+    "    validator.expect_column_distinct_values_to_be_in_set(field_name, unique_field_values[field_name])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# units\n",
+    "validator.expect_column_values_to_be_of_type(\"units\", \"str\")\n",
+    "validator.expect_column_values_to_not_be_null(\"units\")\n",
+    "\n",
+    "# List of accepted values\n",
+    "field_name = \"units\"\n",
+    "if field_name in unique_field_values:\n",
+    "    validator.expect_column_distinct_values_to_be_in_set(field_name, unique_field_values[field_name])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# age_death\n",
+    "validator.expect_column_values_to_be_of_type(\"age_death\", \"int\")\n",
+    "validator.expect_column_values_to_not_be_null(\"age_death\")\n",
+    "validator.expect_column_values_to_be_between(\"age_death\", strict_min_value=0, max_value=100)\n",
+    "\n",
+    "# List of accepted values\n",
+    "field_name = \"age_death\"\n",
+    "if field_name in unique_field_values:\n",
+    "    validator.expect_column_distinct_values_to_be_in_set(field_name, unique_field_values[field_name])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# tissue\n",
+    "validator.expect_column_values_to_be_of_type(\"tissue\", \"str\")\n",
+    "validator.expect_column_values_to_not_be_null(\"tissue\")\n",
+    "\n",
+    "# List of accepted values\n",
+    "field_name = \"tissue\"\n",
+    "if field_name in unique_field_values:\n",
+    "    validator.expect_column_distinct_values_to_be_in_set(field_name, unique_field_values[field_name])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# unique entries ExpectSelectColumnValuesToBeUniqueWithinRecord\n",
+    "validator.expect_select_column_values_to_be_unique_within_record(column_list=[\"model\", \"type\", \"age_death\", \"tissue\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "validator.save_expectation_suite(discard_failed_expectations=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Checkpoint and View Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "checkpoint = context.add_or_update_checkpoint(\n",
+    "    name=\"agora-test-checkpoint\",\n",
+    "    validator=validator,\n",
+    ")\n",
+    "checkpoint_result = checkpoint.run()\n",
+    "context.view_validation_result(checkpoint_result)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Build Data Docs - Click on Expectation Suite to View All Expectations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "context.build_data_docs()\n",
+    "context.open_data_docs()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/modelad_test_config.yaml b/modelad_test_config.yaml
index d09652a2..96e3eac1 100644
--- a/modelad_test_config.yaml
+++ b/modelad_test_config.yaml
@@ -1,7 +1,7 @@
 destination: &dest syn51498092
 staging_path: ./staging
-gx_folder: none
-gx_table: none
+gx_folder: syn63141015
+gx_table: syn63891939
 datasets:
   - biomarkers:
       files:
@@ -15,6 +15,9 @@ datasets:
       custom_transformations: 1
       column_rename:
         agedeath: age_death
+      gx_enabled: true
+      gx_nested_columns:
+        - points
 
   - pathology:
       files:
@@ -28,3 +31,6 @@ datasets:
       custom_transformations: 1
       column_rename:
         agedeath: age_death
+      gx_enabled: true
+      gx_nested_columns:
+        - points
diff --git a/src/agoradatatools/great_expectations/gx/expectations/biomarkers.json b/src/agoradatatools/great_expectations/gx/expectations/biomarkers.json
new file mode 100644
index 00000000..4e7952e5
--- /dev/null
+++ b/src/agoradatatools/great_expectations/gx/expectations/biomarkers.json
@@ -0,0 +1,191 @@
+{
+  "data_asset_type": null,
+  "expectation_suite_name": "biomarkers",
+  "expectations": [
+    {
+      "expectation_type": "expect_column_values_to_be_of_type",
+      "kwargs": {
+        "column": "points",
+        "type_": "str"
+      },
+      "meta": {}
+    },
+    {
+      "expectation_type": "expect_column_values_to_match_json_schema",
+      "kwargs": {
+        "column": "points",
+        "json_schema": {
+          "$id": "http://example.com/example.json",
+          "$schema": "https://json-schema.org/draft/2019-09/schema",
+          "default": [],
+          "items": {
+            "properties": {
+              "genotype": {
+                "type": "string"
+              },
+              "measurement": {
+                "type": [
+                  "number",
+                  "integer"
+                ]
+              },
+              "sex": {
+                "type": "string"
+              }
+            },
+            "required": [
+              "genotype",
+              "measurement",
+              "sex"
+            ],
+            "title": "A Schema",
+            "type": "object"
+          },
+          "title": "Points Schema",
+          "type": "array"
+        }
+      },
+      "meta": {}
+    },
+    {
+      "expectation_type": "expect_column_values_to_be_of_type",
+      "kwargs": {
+        "column": "model",
+        "type_": "str"
+      },
+      "meta": {}
+    },
+    {
+      "expectation_type": "expect_column_values_to_not_be_null",
+      "kwargs": {
+        "column": "model"
+      },
+      "meta": {}
+    },
+    {
+      "expectation_type": "expect_column_distinct_values_to_be_in_set",
+      "kwargs": {
+        "column": "model",
+        "value_set": [
+          "3xTG-AD",
+          "5xFAD",
+          "Trem2-R47H_NSS",
+          "Abca7*v1599M"
+        ]
+      },
+      "meta": {}
+    },
+    {
+      "expectation_type": "expect_column_values_to_be_of_type",
+      "kwargs": {
+        "column": "type",
+        "type_": "str"
+      },
+      "meta": {}
+    },
+    {
+      "expectation_type": "expect_column_values_to_not_be_null",
+      "kwargs": {
+        "column": "type"
+      },
+      "meta": {}
+    },
+    {
+      "expectation_type": "expect_column_distinct_values_to_be_in_set",
+      "kwargs": {
+        "column": "type",
+        "value_set": [
+          "Insoluble Abeta40",
+          "Insoluble Abeta42",
+          "Soluble Abeta40",
+          "Soluble Abeta42",
+          "NfL"
+        ]
+      },
+      "meta": {}
+    },
+    {
+      "expectation_type": "expect_column_values_to_be_of_type",
+      "kwargs": {
+        "column": "units",
+        "type_": "str"
+      },
+      "meta": {}
+    },
+    {
+      "expectation_type": "expect_column_values_to_not_be_null",
+      "kwargs": {
+        "column": "units"
+      },
+      "meta": {}
+    },
+    {
+      "expectation_type": "expect_column_values_to_be_of_type",
+      "kwargs": {
+        "column": "age_death",
+        "type_": "int"
+      },
+      "meta": {}
+    },
+    {
+      "expectation_type": "expect_column_values_to_not_be_null",
+      "kwargs": {
+        "column": "age_death"
+      },
+      "meta": {}
+    },
+    {
+      "expectation_type": "expect_column_values_to_be_between",
+      "kwargs": {
+        "column": "age_death",
+        "max_value": 100,
+        "strict_min_value": 0
+      },
+      "meta": {}
+    },
+    {
+      "expectation_type": "expect_column_values_to_be_of_type",
+      "kwargs": {
+        "column": "tissue",
+        "type_": "str"
+      },
+      "meta": {}
+    },
+    {
+      "expectation_type": "expect_column_values_to_not_be_null",
+      "kwargs": {
+        "column": "tissue"
+      },
+      "meta": {}
+    },
+    {
+      "expectation_type": "expect_column_distinct_values_to_be_in_set",
+      "kwargs": {
+        "column": "tissue",
+        "value_set": [
+          "cerebral cortex",
+          "hippocampus",
+          "plasma"
+        ]
+      },
+      "meta": {}
+    },
+    {
+      "expectation_type": "expect_select_column_values_to_be_unique_within_record",
+      "kwargs": {
+        "column_list": [
+          "model",
+          "type",
+          "age_death",
+          "tissue",
+          "units"
+        ]
+      },
+      "meta": {}
+    }
+  ],
+  "ge_cloud_id": null,
+  "meta": {
+    "great_expectations_version": "0.18.1"
+  }
+}
diff --git a/src/agoradatatools/great_expectations/gx/expectations/pathology.json b/src/agoradatatools/great_expectations/gx/expectations/pathology.json
new file mode 100644
index 00000000..f0c0803d
--- /dev/null
+++ b/src/agoradatatools/great_expectations/gx/expectations/pathology.json
@@ -0,0 +1,193 @@
+{
+  "data_asset_type": null,
+  "expectation_suite_name": "pathology",
+  "expectations": [
+    {
+      "expectation_type": "expect_column_values_to_be_of_type",
+      "kwargs": {
+        "column": "points",
+        "type_": "str"
+      },
+      "meta": {}
+    },
+    {
+      "expectation_type": "expect_column_values_to_match_json_schema",
+      "kwargs": {
+        "column": "points",
+        "json_schema": {
+          "$id": "http://example.com/example.json",
+          "$schema": "https://json-schema.org/draft/2019-09/schema",
+          "default": [],
+          "items": {
+            "properties": {
+              "genotype": {
+                "type": "string"
+              },
+              "measurement": {
+                "type": [
+                  "number",
+                  "integer"
+                ]
+              },
+              "sex": {
+                "type": "string"
+              }
+            },
+            "required": [
+              "genotype",
+              "measurement",
+              "sex"
+            ],
+            "title": "A Schema",
+            "type": "object"
+          },
+          "title": "Points Schema",
+          "type": "array"
+        }
+      },
+      "meta": {}
+    },
+    {
+      "expectation_type": "expect_column_values_to_be_of_type",
+      "kwargs": {
+        "column": "model",
+        "type_": "str"
+      },
+      "meta": {}
+    },
+    {
+      "expectation_type": "expect_column_values_to_not_be_null",
+      "kwargs": {
+        "column": "model"
+      },
+      "meta": {}
+    },
+    {
+      "expectation_type": "expect_column_distinct_values_to_be_in_set",
+      "kwargs": {
+        "column": "model",
+        "value_set": [
+          "3xTG-AD",
+          "5xFAD",
+          "Trem2-R47H_NSS",
+          "Abca7*V1599M"
+        ]
+      },
+      "meta": {}
+    },
+    {
+      "expectation_type": "expect_column_values_to_be_of_type",
+      "kwargs": {
+        "column": "type",
+        "type_": "str"
+      },
+      "meta": {}
+    },
+    {
+      "expectation_type": "expect_column_values_to_not_be_null",
+      "kwargs": {
+        "column": "type"
+      },
+      "meta": {}
+    },
+    {
+      "expectation_type": "expect_column_distinct_values_to_be_in_set",
+      "kwargs": {
+        "column": "type",
+        "value_set": [
+          "Microglia Cell Density (IBA1)",
+          "Plaque Density (Thio-S)",
+          "Plaque Size (Thio-S)",
+          "Astrocyte Cell Density (GFAP)",
+          "Astrocyte Cell Density (S100B)",
+          "Phospho-tau (AT8)",
+          "Dystrophic Neurites (LAMP1)",
+          "Tau (HT7)"
+        ]
+      },
+      "meta": {}
+    },
+    {
+      "expectation_type": "expect_column_values_to_be_of_type",
+      "kwargs": {
+        "column": "units",
+        "type_": "str"
+      },
+      "meta": {}
+    },
+    {
+      "expectation_type": "expect_column_values_to_not_be_null",
+      "kwargs": {
+        "column": "units"
+      },
+      "meta": {}
+    },
+    {
+      "expectation_type": "expect_column_values_to_be_of_type",
+      "kwargs": {
+        "column": "age_death",
+        "type_": "int"
+      },
+      "meta": {}
+    },
+    {
+      "expectation_type": "expect_column_values_to_not_be_null",
+      "kwargs": {
+        "column": "age_death"
+      },
+      "meta": {}
+    },
+    {
+      "expectation_type": "expect_column_values_to_be_between",
+      "kwargs": {
+        "column": "age_death",
+        "max_value": 100,
+        "strict_min_value": 0
+      },
+      "meta": {}
+    },
+    {
+      "expectation_type": "expect_column_values_to_be_of_type",
+      "kwargs": {
+        "column": "tissue",
+        "type_": "str"
+      },
+      "meta": {}
+    },
+    {
+      "expectation_type": "expect_column_values_to_not_be_null",
+      "kwargs": {
+        "column": "tissue"
+      },
+      "meta": {}
+    },
+    {
+      "expectation_type": "expect_column_distinct_values_to_be_in_set",
+      "kwargs": {
+        "column": "tissue",
+        "value_set": [
+          "hippocampus",
+          "cerebral cortex"
+        ]
+      },
+      "meta": {}
+    },
+    {
+      "expectation_type": "expect_select_column_values_to_be_unique_within_record",
+      "kwargs": {
+        "column_list": [
+          "model",
+          "type",
+          "age_death",
+          "tissue",
+          "units"
+        ]
+      },
+      "meta": {}
+    }
+  ],
+  "ge_cloud_id": null,
+  "meta": {
+    "great_expectations_version": "0.18.1"
+  }
+}
diff --git a/src/agoradatatools/great_expectations/gx/json_schemas/immunohisto/biomarkers_unique_field_values.json b/src/agoradatatools/great_expectations/gx/json_schemas/immunohisto/biomarkers_unique_field_values.json
new file mode 100644
index 00000000..25aac928
--- /dev/null
+++ b/src/agoradatatools/great_expectations/gx/json_schemas/immunohisto/biomarkers_unique_field_values.json
@@ -0,0 +1,24 @@
+{
+    "model": [
+        "3xTG-AD",
+        "5xFAD",
+        "Trem2-R47H_NSS",
+        "Abca7*v1599M"
+    ],
+    "type": [
+        "Insoluble Abeta40",
+        "Insoluble Abeta42",
+        "Soluble Abeta40",
+        "Soluble Abeta42",
+        "NfL"
+    ],
+    "tissue": [
+        "cerebral cortex",
+        "hippocampus",
+        "plasma"
+    ],
+    "sex": [
+        "female",
+        "male"
+    ]
+}
diff --git a/src/agoradatatools/great_expectations/gx/json_schemas/immunohisto/generate_field_validation_lists.ipynb b/src/agoradatatools/great_expectations/gx/json_schemas/immunohisto/generate_field_validation_lists.ipynb
new file mode 100644
index 00000000..6b17e27c
--- /dev/null
+++ b/src/agoradatatools/great_expectations/gx/json_schemas/immunohisto/generate_field_validation_lists.ipynb
@@ -0,0 +1,149 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Generate Jsons of Expected Field Values for Datasets\n",
+    "We need to identify and standardize all possible values for a set of fields in the MODEL-AD immunohisto data (biomarkers and pathology - or more). Although it is not ideal to create a validation set directly from the data, we decided it would be best to start here and manually update the lists as needed.\n",
+    "\n",
+    "#### General steps:\n",
+    "1. Define your datasets\n",
+    "2. Download the data\n",
+    "3. Get unique set of values for each field of interest\n",
+    "4. Output the information in a json to be read during gx validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "from agoradatatools.etl import extract, utils"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### User specified values\n",
+    "You must create a dataset object with the following structure.\n",
+    "```\n",
+    "datasets = {\n",
+    "    \"dataset_name\": {\n",
+    "        \"synapse_id\": \"ID\",\n",
+    "        \"fields\": {\n",
+    "            \"field_name\": [],\n",
+    "            \"field_name\": [],\n",
+    "            ...\n",
+    "        }\n",
+    "    },\n",
+    "    ...\n",
+    "}\n",
+    "```\n",
+    "If you want to extract more fields, add '\"field_name\": []' to the \"fields\" dictionary. After running the notebook, the unique field values will be stored as a list for each field."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# User specified values\n",
+    "\n",
+    "datasets = {\n",
+    "    \"biomarkers\": {\n",
+    "        \"synapse_id\": \"syn61250724.1\",\n",
+    "        \"fields\": {\n",
+    "            \"model\": [],\n",
+    "            \"type\": [],\n",
+    "            \"tissue\": [],\n",
+    "            \"sex\": []\n",
+    "        }\n",
+    "    },\n",
+    "    \"pathology\": {\n",
+    "        \"synapse_id\": \"syn61357279\",\n",
+    "        \"fields\": {\n",
+    "            \"model\": [],\n",
+    "            \"type\": [],\n",
+    "            \"tissue\": [],\n",
+    "            \"sex\": []\n",
+    "        }\n",
+    "    }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Log into Synapse\n",
+    "syn = utils._login_to_synapse()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Download data as dataframes\n",
+    "for dataset in datasets:\n",
+    "    df = extract.get_entity_as_df(syn_id=datasets[dataset][\"synapse_id\"], source=\"csv\", syn=syn)\n",
+    "    df = utils.standardize_column_names(df=df)\n",
+    "    df = utils.standardize_values(df=df)\n",
+    "    datasets[dataset][\"df\"] = df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get unique values for each field\n",
+    "for dataset in datasets:\n",
+    "    for field in datasets[dataset][\"fields\"]:\n",
+    "        datasets[dataset][\"fields\"][field] = datasets[dataset][\"df\"][field].unique().tolist()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Write to json\n",
+    "for dataset in datasets:\n",
+    "    with open(f\"{dataset}_unique_field_values.json\", \"w\") as f:\n",
+    "        json.dump(datasets[dataset][\"fields\"], f, indent=4)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "synapseclient",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/agoradatatools/great_expectations/gx/json_schemas/immunohisto/pathology_unique_field_values.json b/src/agoradatatools/great_expectations/gx/json_schemas/immunohisto/pathology_unique_field_values.json
new file mode 100644
index 00000000..ebb6d16a
--- /dev/null
+++ b/src/agoradatatools/great_expectations/gx/json_schemas/immunohisto/pathology_unique_field_values.json
@@ -0,0 +1,26 @@
+{
+    "model": [
+        "3xTG-AD",
+        "5xFAD",
+        "Trem2-R47H_NSS",
+        "Abca7*V1599M"
+    ],
+    "type": [
+        "Microglia Cell Density (IBA1)",
+        "Plaque Density (Thio-S)",
+        "Plaque Size (Thio-S)",
+        "Astrocyte Cell Density (GFAP)",
+        "Astrocyte Cell Density (S100B)",
+        "Phospho-tau (AT8)",
+        "Dystrophic Neurites (LAMP1)",
+        "Tau (HT7)"
+    ],
+    "tissue": [
+        "hippocampus",
+        "cerebral cortex"
+    ],
+    "sex": [
+        "female",
+        "male"
+    ]
+}
diff --git a/src/agoradatatools/great_expectations/gx/json_schemas/immunohisto/points.json b/src/agoradatatools/great_expectations/gx/json_schemas/immunohisto/points.json
new file mode 100644
index 00000000..73fbbf1a
--- /dev/null
+++ b/src/agoradatatools/great_expectations/gx/json_schemas/immunohisto/points.json
@@ -0,0 +1,30 @@
+{
+    "$schema": "https://json-schema.org/draft/2019-09/schema",
+    "$id": "http://example.com/example.json",
+    "type": "array",
+    "default": [],
+    "title": "Points Schema",
+    "items": {
+        "type": "object",
+        "title": "A Schema",
+        "required": [
+            "genotype",
+            "measurement",
+            "sex"
+        ],
+        "properties": {
+            "genotype": {
+                "type": "string"
+            },
+            "measurement": {
+                "type": [
+                    "number",
+                    "integer"
+                ]
+            },
+            "sex": {
+                "type": "string"
+            }
+        }
+    }
+}