diff --git a/doc/output.rst b/doc/output.rst
index 989baefdc..0cbfa9bf6 100644
--- a/doc/output.rst
+++ b/doc/output.rst
@@ -254,6 +254,11 @@ to read data using DuckDB. These include:
   Analysis scripts (see :ref:`analysis_scripts`) receive a ``history_sql`` and
   ``config_sql`` that reads data from Parquet files with filters applied when
   run using :py:mod:`runscripts.analysis`.
+- :py:func:`~ecoli.library.parquet_emitter.quote_columns`: Enclose
+  raw column names in double quotes to handle special characters (e.g. spaces,
+  dashes, etc.) when constructing DuckDB SQL queries.
+- :py:func:`~ecoli.library.parquet_emitter.list_columns`: Get a list of all
+  output column names, optionally filtered by glob pattern.
 - :py:func:`~ecoli.library.parquet_emitter.union_by_name`: Modify SQL query
   from :py:func:`~ecoli.library.parquet_emitter.dataset_sql` to
   use DuckDB's `union_by_name <https://duckdb.org/docs/stable/data/multiple_files/combining_schemas.html#union-by-name>`_.
diff --git a/ecoli/library/parquet_emitter.py b/ecoli/library/parquet_emitter.py
index 0eeafa2a7..5a1551d46 100644
--- a/ecoli/library/parquet_emitter.py
+++ b/ecoli/library/parquet_emitter.py
@@ -1,4 +1,5 @@
 import os
+import fnmatch
 from concurrent.futures import Future, ThreadPoolExecutor
 from typing import Any, Callable, cast, Mapping, Optional
 from urllib import parse
@@ -180,6 +181,43 @@ def dataset_sql(out_dir: str, experiment_ids: list[str]) -> tuple[str, str, str]
     return sql_queries[0], sql_queries[1], sql_queries[2]
 
 
+def list_columns(
+    conn: duckdb.DuckDBPyConnection, history_subquery: str, pattern: str | None = None
+) -> list[str]:
+    """
+    Return list of columns in DuckDB subquery containing sim output data.
+
+    Args:
+        conn: DuckDB connection
+        history_subquery: DuckDB query containing sim output data
+        pattern: Optional glob pattern to filter column names
+    """
+    columns = (
+        conn.sql(f"SELECT column_name FROM (DESCRIBE ({history_subquery}))")
+        .pl()["column_name"]
+        .to_list()
+    )
+    if pattern is not None:
+        columns = fnmatch.filter(columns, pattern)
+    return columns
+
+
+def quote_columns(columns: str | list[str]) -> str | list[str]:
+    """
+    Given one or more raw column names (not DuckDB expressions),
+    return the same column name(s) enclosed in
+    double quotes to handle special characters (spaces, dashes, etc.).
+
+    Args:
+        columns: One or more column names
+    """
+    if isinstance(columns, str):
+        # Escape existing double quotes by doubling them
+        escaped = columns.replace('"', '""')
+        return f'"{escaped}"'
+    return [cast(str, quote_columns(col)) for col in columns]
+
+
 def num_cells(conn: duckdb.DuckDBPyConnection, subquery: str) -> int:
     """
     Return cell count in DuckDB subquery containing ``experiment_id``,
@@ -524,10 +562,16 @@ def read_stacked_columns(
     also include the ``experiment_id``, ``variant``, ``lineage_seed``,
     ``generation``, ``agent_id``, and ``time`` columns.
 
-    .. warning:: If the column expressions in ``columns`` are not from
+    .. hint:: To get a full list of columns in the output data that you can
+        use in your ``columns`` SQL expressions, use :py:func:`~.list_columns`.
+
+    .. warning:: If your raw column names contain special characters and you
+        are not constructing your column expressions with
         :py:func:`~named_idx` or :py:func:`~ndidx_to_duckdb_expr`,
-        they may need to be enclosed in double quotes to handle
-        special characters (e.g. ``"col-with-hyphens"``).
+        the raw column names MUST be enclosed in double quotes
+        to handle special characters (e.g. ``'"space and-hyphens"'``,
+        ``"\"[brackets]\""``). Use :py:func:`~quote_columns` to quote
+        these columns before constructing SQL expressions with them.
 
     For example, to get the average total concentration of three bulk molecules
     with indices 100, 1000, and 10000 per cell::
diff --git a/ecoli/library/test_parquet_emitter.py b/ecoli/library/test_parquet_emitter.py
index 670306748..19c4d2753 100644
--- a/ecoli/library/test_parquet_emitter.py
+++ b/ecoli/library/test_parquet_emitter.py
@@ -21,6 +21,9 @@
     flatten_dict,
     union_pl_dtypes,
     ParquetEmitter,
+    quote_columns,
+    list_columns,
+    create_duckdb_conn,
 )
 
 
@@ -251,6 +254,154 @@ def test_union_pl_dtypes(self):
             pl.UInt32,
         ) == pl.List(pl.List(pl.List(pl.UInt32)))
 
+    def test_quote_columns(self):
+        """Test quote_columns handles special characters correctly."""
+        # Test single string with special characters
+        assert quote_columns("simple") == '"simple"'
+        assert quote_columns("with spaces") == '"with spaces"'
+        assert quote_columns("with-hyphens") == '"with-hyphens"'
+        assert quote_columns("with[brackets]") == '"with[brackets]"'
+        assert quote_columns("with/slashes") == '"with/slashes"'
+
+        # Test string with existing double quotes (should be escaped)
+        assert quote_columns('already"quoted') == '"already""quoted"'
+        assert quote_columns('"fully"quoted"') == '"""fully""quoted"""'
+
+        # Test list of strings
+        assert quote_columns(["col1", "col2", "col3"]) == [
+            '"col1"',
+            '"col2"',
+            '"col3"',
+        ]
+        assert quote_columns(["with spaces", "with-hyphens"]) == [
+            '"with spaces"',
+            '"with-hyphens"',
+        ]
+
+        # Test mixed special characters in list
+        assert quote_columns(["normal", "space here", "hyphen-here", 'quote"here']) == [
+            '"normal"',
+            '"space here"',
+            '"hyphen-here"',
+            '"quote""here"',
+        ]
+
+        # Test empty cases
+        assert quote_columns("") == '""'
+        assert quote_columns([]) == []
+
+        # Test that quoted columns actually work in DuckDB queries with weird column names
+        with tempfile.TemporaryDirectory() as tmp_path:
+            test_file = os.path.join(tmp_path, "weird_cols.parquet")
+            # Create test data with columns containing special characters
+            test_data = pl.DataFrame(
+                {
+                    "simple": [1, 2, 3],
+                    "with spaces": [4, 5, 6],
+                    "with-hyphens": [7, 8, 9],
+                    "with[brackets]": [10, 11, 12],
+                    "with/slashes": [13, 14, 15],
+                    'has"quote': [16, 17, 18],
+                    "dot.name": [19, 20, 21],
+                    "colon:name": [22, 23, 24],
+                }
+            )
+            test_data.write_parquet(test_file, statistics=False)
+
+            conn = create_duckdb_conn()
+
+            # Test selecting individual columns with special characters
+            for col in test_data.columns:
+                quoted_col = quote_columns(col)
+                result = conn.sql(f"SELECT {quoted_col} FROM '{test_file}'").pl()
+                assert result.shape == (3, 1)
+                assert result.columns[0] == col
+                expected_values = test_data[col].to_list()
+                assert result[col].to_list() == expected_values
+
+            # Test selecting multiple columns at once
+            weird_cols = ["with spaces", "with-hyphens", "with[brackets]", 'has"quote']
+            quoted_cols = ", ".join(quote_columns(weird_cols))
+            result = conn.sql(f"SELECT {quoted_cols} FROM '{test_file}'").pl()
+            assert result.shape == (3, 4)
+            for col in weird_cols:
+                assert col in result.columns
+                assert result[col].to_list() == test_data[col].to_list()
+
+            # Test that using WHERE clause works with quoted columns
+            quoted_space_col = quote_columns("with spaces")
+            result = conn.sql(
+                f"SELECT * FROM '{test_file}' WHERE {quoted_space_col} > 4"
+            ).pl()
+            assert result.shape == (2, 8)
+            assert result["with spaces"].to_list() == [5, 6]
+
+            # Test aggregation with quoted columns
+            quoted_bracket_col = quote_columns("with[brackets]")
+            result = conn.sql(
+                f"SELECT AVG({quoted_bracket_col}) as avg_val FROM '{test_file}'"
+            ).pl()
+            assert result["avg_val"][0] == 11.0
+
+            # Test ORDER BY with quoted columns
+            quoted_slash_col = quote_columns("with/slashes")
+            result = conn.sql(
+                f"SELECT {quoted_slash_col} FROM '{test_file}' ORDER BY {quoted_slash_col} DESC"
+            ).pl()
+            assert result["with/slashes"].to_list() == [15, 14, 13]
+
+    def test_list_columns(self):
+        """Test list_columns retrieves column names correctly."""
+        with tempfile.TemporaryDirectory() as tmp_path:
+            # Create test Parquet file with known columns
+            test_file = os.path.join(tmp_path, "test.parquet")
+            test_data = pl.DataFrame(
+                {
+                    "col_a": [1, 2, 3],
+                    "col_b": [4.0, 5.0, 6.0],
+                    "listeners__mass__cell_mass": [7.0, 8.0, 9.0],
+                    "listeners__mass__dry_mass": [10.0, 11.0, 12.0],
+                    "listeners__growth__instantaneous_growth_rate": [0.1, 0.2, 0.3],
+                    "bulk": [[1, 2], [3, 4], [5, 6]],
+                }
+            )
+            test_data.write_parquet(test_file, statistics=False)
+
+            conn = create_duckdb_conn()
+            subquery = f"SELECT * FROM '{test_file}'"
+
+            # Test getting all columns
+            all_cols = list_columns(conn, subquery)
+            assert len(all_cols) == 6
+            assert "col_a" in all_cols
+            assert "col_b" in all_cols
+            assert "listeners__mass__cell_mass" in all_cols
+
+            # Test pattern matching with glob patterns
+            listener_cols = list_columns(conn, subquery, "listeners__*")
+            assert len(listener_cols) == 3
+            assert all(col.startswith("listeners__") for col in listener_cols)
+
+            # Test pattern matching for specific listener
+            mass_cols = list_columns(conn, subquery, "listeners__mass__*")
+            assert len(mass_cols) == 2
+            assert "listeners__mass__cell_mass" in mass_cols
+            assert "listeners__mass__dry_mass" in mass_cols
+
+            # Test pattern that matches nothing
+            no_match = list_columns(conn, subquery, "nonexistent__*")
+            assert len(no_match) == 0
+
+            # Test pattern with single character wildcard
+            col_pattern = list_columns(conn, subquery, "col_?")
+            assert len(col_pattern) == 2
+            assert "col_a" in col_pattern
+            assert "col_b" in col_pattern
+
+            # Test exact match pattern
+            exact = list_columns(conn, subquery, "bulk")
+            assert exact == ["bulk"]
+
 
 def compare_nested(a: list, b: list) -> bool:
     """