diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
index 8c4f5482..5b0e8983 100644
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -85,7 +85,7 @@ jobs:
         with:
           directory: "./doc/_build/html"
           # The directory to scan
-          arguments: --checks Links,Scripts --ignore-urls "https://fonts.gstatic.com,https://mkdocs-gallery.github.io,./doc/_build/html/_static/" --assume-extension --check-external-hash --ignore-status-codes 403 --ignore-files "/.+\/html\/_static\/.+/"
+          arguments: --checks Links,Scripts --ignore-urls "https://fonts.gstatic.com,https://mkdocs-gallery.github.io,./doc/_build/html/_static/,https://www.nature.com/articles/s41593-022-01020-w" --assume-extension --check-external-hash --ignore-status-codes 403 --ignore-files "/.+\/html\/_static\/.+/"
           # The arguments to pass to HTMLProofer
 
 
diff --git a/pynapple/core/time_series.py b/pynapple/core/time_series.py
index 72d33d5a..fbb3d9ee 100644
--- a/pynapple/core/time_series.py
+++ b/pynapple/core/time_series.py
@@ -1169,23 +1169,24 @@ def __getitem__(self, key, *args, **kwargs):
                 index = np.array([index])
 
             if all(is_array_like(a) for a in [index, output]):
-                if (
-                    (len(index) == 1)
-                    and (output.ndim == 1)
-                    and ((len(output) > 1) or isinstance(key[1], (list, np.ndarray)))
-                ):
-                    # reshape output of single index to preserve column axis if there are more than one columns being indexed
-                    # or if column key is a list or array
+                if isinstance(key, tuple):
+                    if (
+                        len(index) == 1
+                        and output.ndim == 1
+                        and not isinstance(key[1], int)
+                    ):
+                        output = output[None, :]
+                    elif (
+                        (output.ndim == 1)
+                        and isinstance(key[1], (list, np.ndarray))
+                        and (len(columns) == 1)
+                    ):
+                        # reshape output of single column if column key is a list or array
+                        output = output[:, None]
+                # if getting a row (1 dim implied)
+                elif isinstance(key, Number):
                     output = output[None, :]
 
-                elif (
-                    (output.ndim == 1)
-                    and isinstance(key[1], (list, np.ndarray))
-                    and (len(columns) == 1)
-                ):
-                    # reshape output of single column if column key is a list or array
-                    output = output[:, None]
-
                 kwargs["columns"] = columns
                 kwargs["metadata"] = self._metadata.loc[columns]
                 return _initialize_tsd_output(
diff --git a/tests/test_time_series.py b/tests/test_time_series.py
index 0c5e9289..354104ed 100755
--- a/tests/test_time_series.py
+++ b/tests/test_time_series.py
@@ -954,6 +954,7 @@ def test_interpolate_with_ep(self, tsd):
 @pytest.mark.parametrize(
     "tsdframe",
     [
+        nap.TsdFrame(t=np.arange(100), d=np.random.rand(100, 1), time_units="s"),
         nap.TsdFrame(t=np.arange(100), d=np.random.rand(100, 3), time_units="s"),
         nap.TsdFrame(
             t=np.arange(100),
@@ -997,6 +998,7 @@ def test_copy(self, tsdframe):
         ],
     )
     def test_horizontal_slicing(self, tsdframe, index, nap_type):
+        index = index if isinstance(index, int) else index[: tsdframe.shape[1]]
         assert isinstance(tsdframe[:, index], nap_type)
         np.testing.assert_array_almost_equal(
             tsdframe[:, index].values, tsdframe.values[:, index]
@@ -1067,6 +1069,12 @@ def test_vertical_slicing(self, tsdframe, index):
         ],
     )
     def test_vert_and_horz_slicing(self, tsdframe, row, col, expected):
+        if tsdframe.shape[1] == 1:
+            if isinstance(col, list) and isinstance(col[0], int):
+                col = [0]
+            elif isinstance(col, list) and isinstance(col[0], bool):
+                col = [col[0]]
+
         # get details about row index
         row_array = isinstance(row, (list, np.ndarray))
         if row_array and isinstance(row[0], (bool, np.bool_)):