diff --git a/requirements.txt b/requirements.txt
index 6db805df..3f04f9e5 100755
--- a/requirements.txt
+++ b/requirements.txt
@@ -4,7 +4,7 @@ colorcet>=2.0.1
 cvxopt>=1.2.3
 csbdeep>=0.6.3
 descartes
-dynamo-release>=1.3.0
+dynamo-release>=1.4.1
 folium>=0.12.1
 geopandas>=0.10.2
 gpytorch
@@ -14,7 +14,7 @@ kornia>=0.6.4
 leidenalg>=0.10.0
 loompy>=3.0.5
 mapclassify>=2.4.2
-matplotlib<=3.5.3
+matplotlib>=3.7.5
 nbconvert
 networkx>=2.6.3
 # ngs_tools>=1.6.0
diff --git a/spateo/configuration.py b/spateo/configuration.py
index 4b60fb0d..27e49e14 100755
--- a/spateo/configuration.py
+++ b/spateo/configuration.py
@@ -7,7 +7,7 @@
 from typing import List, Optional, Tuple, Union
 
 import colorcet
-import matplotlib
+import matplotlib as mpl
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
@@ -247,12 +247,12 @@ def get_agg_bounds(adata: AnnData) -> Tuple[int, int, int, int]:
 
 # Means to shift the scale of colormaps:
 def shiftedColorMap(
-    cmap: matplotlib.colors.ListedColormap,
+    cmap: mpl.colors.ListedColormap,
     start: float = 0,
     midpoint: float = 0.5,
     stop: float = 1.0,
     name: str = "shiftedcmap",
-) -> matplotlib.colors.ListedColormap:
+) -> mpl.colors.ListedColormap:
     """
     Function to offset the "center" of a colormap. Useful for
     data with a negative min and positive max, and you want the
@@ -279,7 +279,7 @@ def shiftedColorMap(
         newcmap: a new colormap that has the middle point of the colormap shifted.
     """
     # Check for existing shifted colormap:
-    matplotlib.cm.ColormapRegistry.unregister(plt.colormaps, name="shiftedcmap")
+    mpl.cm.ColormapRegistry.unregister(plt.colormaps, name="shiftedcmap")
 
     cdict = {"red": [], "green": [], "blue": [], "alpha": []}
 
@@ -299,54 +299,48 @@ def shiftedColorMap(
         cdict["blue"].append((si, b, b))
         cdict["alpha"].append((si, a, a))
 
-    newcmap = matplotlib.colors.LinearSegmentedColormap(name, cdict)
-    plt.register_cmap(cmap=newcmap)
+    newcmap = mpl.colors.LinearSegmentedColormap(name, cdict)
+    mpl.colormaps.register(cmap=newcmap)
 
     return newcmap
 
 
-fire_cmap = matplotlib.colors.LinearSegmentedColormap.from_list("fire", colorcet.fire)
-darkblue_cmap = matplotlib.colors.LinearSegmentedColormap.from_list("darkblue", colorcet.kbc)
-darkgreen_cmap = matplotlib.colors.LinearSegmentedColormap.from_list("darkgreen", colorcet.kgy)
-darkred_cmap = matplotlib.colors.LinearSegmentedColormap.from_list(
-    "darkred", colors=colorcet.linear_kry_5_95_c72[:192], N=256
-)
-darkpurple_cmap = matplotlib.colors.LinearSegmentedColormap.from_list("darkpurple", colorcet.linear_bmw_5_95_c89)
+fire_cmap = mpl.colors.LinearSegmentedColormap.from_list("fire", colorcet.fire)
+darkblue_cmap = mpl.colors.LinearSegmentedColormap.from_list("darkblue", colorcet.kbc)
+darkgreen_cmap = mpl.colors.LinearSegmentedColormap.from_list("darkgreen", colorcet.kgy)
+darkred_cmap = mpl.colors.LinearSegmentedColormap.from_list("darkred", colors=colorcet.linear_kry_5_95_c72[:192], N=256)
+darkpurple_cmap = mpl.colors.LinearSegmentedColormap.from_list("darkpurple", colorcet.linear_bmw_5_95_c89)
 # add gkr theme
-div_blue_black_red_cmap = matplotlib.colors.LinearSegmentedColormap.from_list(
+div_blue_black_red_cmap = mpl.colors.LinearSegmentedColormap.from_list(
     "div_blue_black_red", colorcet.diverging_gkr_60_10_c40
 )
 # add RdBu_r theme
-div_blue_red_cmap = matplotlib.colors.LinearSegmentedColormap.from_list(
-    "div_blue_red", colorcet.diverging_bwr_55_98_c37
-)
+div_blue_red_cmap = mpl.colors.LinearSegmentedColormap.from_list("div_blue_red", colorcet.diverging_bwr_55_98_c37)
 # add glasbey_bw for cell annotation in white background
-glasbey_white_cmap = matplotlib.colors.LinearSegmentedColormap.from_list("glasbey_white", colorcet.glasbey_bw_minc_20)
+glasbey_white_cmap = mpl.colors.LinearSegmentedColormap.from_list("glasbey_white", colorcet.glasbey_bw_minc_20)
 # add glasbey_bw_minc_20_maxl_70 theme for cell annotation in dark background
-glasbey_dark_cmap = matplotlib.colors.LinearSegmentedColormap.from_list(
-    "glasbey_dark", colorcet.glasbey_bw_minc_20_maxl_70
-)
+glasbey_dark_cmap = mpl.colors.LinearSegmentedColormap.from_list("glasbey_dark", colorcet.glasbey_bw_minc_20_maxl_70)
 
 with warnings.catch_warnings():
     warnings.simplefilter("ignore")
-    if "fire" not in matplotlib.colormaps():
-        plt.register_cmap("fire", fire_cmap)
-    if "darkblue" not in matplotlib.colormaps():
-        plt.register_cmap("darkblue", darkblue_cmap)
-    if "darkgreen" not in matplotlib.colormaps():
-        plt.register_cmap("darkgreen", darkgreen_cmap)
-    if "darkred" not in matplotlib.colormaps():
-        plt.register_cmap("darkred", darkred_cmap)
-    if "darkpurple" not in matplotlib.colormaps():
-        plt.register_cmap("darkpurple", darkpurple_cmap)
-    if "div_blue_black_red" not in matplotlib.colormaps():
-        plt.register_cmap("div_blue_black_red", div_blue_black_red_cmap)
-    if "div_blue_red" not in matplotlib.colormaps():
-        plt.register_cmap("div_blue_red", div_blue_red_cmap)
-    if "glasbey_white" not in matplotlib.colormaps():
-        plt.register_cmap("glasbey_white", glasbey_white_cmap)
-    if "glasbey_dark" not in matplotlib.colormaps():
-        plt.register_cmap("glasbey_dark", glasbey_dark_cmap)
+    if "fire" not in mpl.colormaps():
+        mpl.colormaps.register(cmap=fire_cmap, name="fire")
+    if "darkblue" not in mpl.colormaps():
+        mpl.colormaps.register(cmap=darkblue_cmap, name="darkblue")
+    if "darkgreen" not in mpl.colormaps():
+        mpl.colormaps.register(cmap=darkgreen_cmap, name="darkgreen")
+    if "darkred" not in mpl.colormaps():
+        mpl.colormaps.register(cmap=darkred_cmap, name="darkred")
+    if "darkpurple" not in mpl.colormaps():
+        mpl.colormaps.register(cmap=darkpurple_cmap, name="darkpurple")
+    if "div_blue_black_red" not in mpl.colormaps():
+        mpl.colormaps.register(cmap=div_blue_black_red_cmap, name="div_blue_black_red")
+    if "div_blue_red" not in mpl.colormaps():
+        mpl.colormaps.register(cmap=div_blue_red_cmap, name="div_blue_red")
+    if "glasbey_white" not in mpl.colormaps():
+        mpl.colormaps.register(cmap=glasbey_white_cmap, name="glasbey_white")
+    if "glasbey_dark" not in mpl.colormaps():
+        mpl.colormaps.register(cmap=glasbey_dark_cmap, name="glasbey_dark")
 
 _themes = {
     "fire": {
@@ -512,7 +506,7 @@ def config_spateo_rcParams(
     background: str = "white",
     prop_cycle: List[str] = zebrafish_256,
     fontsize: int = 8,
-    color_map: matplotlib.colors.ListedColormap = None,
+    color_map: mpl.colors.ListedColormap = None,
     frameon: Optional[bool] = None,
 ) -> None:
     """Configure matplotlib.rcParams to spateo defaults (based on ggplot style and scanpy).
@@ -735,8 +729,8 @@ def set_pub_style(scaler: float = 1) -> None:
     """
 
     set_figure_params("spateo", background="white")
-    matplotlib.use("cairo")
-    matplotlib.rcParams.update({"font.size": 6 * scaler})
+    mpl.use("cairo")
+    mpl.rcParams.update({"font.size": 6 * scaler})
     params = {
         "font.size": 6 * scaler,
         "legend.fontsize": 6 * scaler,
@@ -748,13 +742,13 @@ def set_pub_style(scaler: float = 1) -> None:
         "axes.titlepad": 1 * scaler,
         "axes.labelpad": 1 * scaler,
     }
-    matplotlib.rcParams.update(params)
+    mpl.rcParams.update(params)
 
 
 def set_pub_style_mpltex():
     """formatting helper function based on mpltex package that can be used to save publishable figures"""
     set_figure_params("spateo", background="white")
-    matplotlib.use("cairo")
+    mpl.use("cairo")
     # the following code is adapted from https://github.com/liuyxpp/mpltex
     # latex_preamble = r"\usepackage{siunitx}\sisetup{detect-all}\usepackage{helvet}\usepackage[eulergreek,EULERGREEK]{sansmath}\sansmath"
     params = {
@@ -811,4 +805,4 @@ def set_pub_style_mpltex():
         "axes.titlepad": 1,
         "axes.labelpad": 1,
     }
-    matplotlib.rcParams.update(params)
+    mpl.rcParams.update(params)
diff --git a/spateo/digitization/contour.py b/spateo/digitization/contour.py
index edb4e6ef..2b1b303e 100644
--- a/spateo/digitization/contour.py
+++ b/spateo/digitization/contour.py
@@ -41,6 +41,7 @@ def gen_cluster_image(
             prepared from the designated cmap.
     """
 
+    import matplotlib as mpl
     import matplotlib.pyplot as plt
 
     if bin_size is None:
@@ -49,7 +50,7 @@ def gen_cluster_image(
     lm.main_info(f"Set up the color for the clusters with the {cmap} colormap.")
 
     # TODO: what if cluster number is larger than cmap.N?
-    cmap = plt.cm.get_cmap(cmap)
+    cmap = mpl.colormaps[cmap]
     colors = cmap(np.arange(cmap.N))
     color_ls = []
     for i in range(cmap.N):
diff --git a/spateo/plotting/static/interactions.py b/spateo/plotting/static/interactions.py
index a4f50ece..8c456c09 100644
--- a/spateo/plotting/static/interactions.py
+++ b/spateo/plotting/static/interactions.py
@@ -467,7 +467,7 @@ def plot_connections(
 
     # Set label colors:
     if isinstance(colormap, str):
-        cmap = mpl.cm.get_cmap(colormap)
+        cmap = mpl.colormaps[colormap]
     else:
         cmap = colormap
 
diff --git a/spateo/plotting/static/scatters.py b/spateo/plotting/static/scatters.py
index 29361550..b590d087 100644
--- a/spateo/plotting/static/scatters.py
+++ b/spateo/plotting/static/scatters.py
@@ -346,6 +346,7 @@ def scatters(
             then this will simply display inline.
     """
 
+    import matplotlib as mpl
     import matplotlib.pyplot as plt
     from matplotlib import rcParams
     from matplotlib.colors import rgb2hex, to_hex
@@ -776,7 +777,7 @@ def _plot_basis_layer(cur_b, cur_l):
                 if stack_colors:
                     # main_debug("stack colors: changing cmap")
                     _cmap = stack_colors_cmaps[ax_index % len(stack_colors_cmaps)]
-                    max_color = matplotlib.cm.get_cmap(_cmap)(float("inf"))
+                    max_color = mpl.colormaps[_cmap](float("inf"))
                     legend_circle = Line2D(
                         [0],
                         [0],
diff --git a/spateo/plotting/static/three_d_plot/three_dims_plots.py b/spateo/plotting/static/three_d_plot/three_dims_plots.py
index efcad8bc..4d45bff8 100644
--- a/spateo/plotting/static/three_d_plot/three_dims_plots.py
+++ b/spateo/plotting/static/three_d_plot/three_dims_plots.py
@@ -1251,7 +1251,7 @@ def visualize_3D_increasing_direction_gradient(
             1 - (1 - coords_norm) * (1 - new_center) / 0.5,  # Compress the upper half
         )
 
-    colors = mpl.cm.get_cmap(cmap)(coords_norm)
+    colors = mpl.colormaps[cmap](coords_norm)
     # Convert colors to hex format:
     colors = ["#" + "".join([f"{int(c * 255):02x}" for c in color[:3]]) for color in colors]
 
diff --git a/spateo/plotting/static/utils.py b/spateo/plotting/static/utils.py
index 697f7ee6..87e6951e 100755
--- a/spateo/plotting/static/utils.py
+++ b/spateo/plotting/static/utils.py
@@ -10,7 +10,7 @@
     warnings.simplefilter("ignore")
     import geopandas as gpd
 
-import matplotlib
+import matplotlib as mpl
 import matplotlib.patheffects as PathEffects
 import matplotlib.pyplot as plt
 import mpl_toolkits
@@ -75,15 +75,13 @@ def _get_adata_color_vec(adata, layer, col):
 
 
 def map2color(val, min=None, max=None, cmap="viridis"):
-    import matplotlib
     import matplotlib.cm as cm
-    import matplotlib.pyplot as plt
 
     minima = np.min(val) if min is None else min
     maxima = np.max(val) if max is None else max
 
-    norm = matplotlib.colors.Normalize(vmin=minima, vmax=maxima, clip=True)
-    mapper = cm.ScalarMappable(norm=norm, cmap=plt.get_cmap(cmap))
+    norm = mpl.colors.Normalize(vmin=minima, vmax=maxima, clip=True)
+    mapper = cm.ScalarMappable(norm=norm, cmap=mpl.colormaps[cmap])
 
     cols = [mapper.to_rgba(v) for v in val]
 
@@ -91,7 +89,7 @@ def map2color(val, min=None, max=None, cmap="viridis"):
 
 
 def _to_hex(arr):
-    return [matplotlib.colors.to_hex(c) for c in arr]
+    return [mpl.colors.to_hex(c) for c in arr]
 
 
 # https://stackoverflow.com/questions/8468855/convert-a-rgb-colour-value-to-decimal
@@ -399,7 +397,7 @@ def _matplotlib_points(
             )
         if color_key is None:
             # main_debug("color_key is None")
-            cmap = copy.copy(matplotlib.cm.get_cmap(color_key_cmap))
+            cmap = copy.copy(mpl.colormaps[color_key_cmap])
             cmap.set_bad("lightgray")
             colors = None
 
@@ -594,12 +592,12 @@ def _matplotlib_points(
     # Color by values
     elif values is not None:
         # main_debug("drawing points by values")
-        cmap_ = copy.copy(matplotlib.cm.get_cmap(cmap))
+        cmap_ = copy.copy(mpl.colormaps[cmap])
         cmap_.set_bad("lightgray")
 
         with warnings.catch_warnings():
             warnings.simplefilter("ignore")
-            matplotlib.cm.register_cmap(name=cmap_.name, cmap=cmap_, override_builtin=True)
+            mpl.colormaps.register(name=cmap_.name, cmap=cmap_, force=True)
 
         if values.shape[0] != points.shape[0]:
             raise ValueError(
@@ -780,9 +778,9 @@ def _matplotlib_points(
         if "norm" in kwargs:
             norm = kwargs["norm"]
         else:
-            norm = matplotlib.colors.Normalize(vmin=_vmin, vmax=_vmax)
+            norm = mpl.colors.Normalize(vmin=_vmin, vmax=_vmax)
 
-        mappable = matplotlib.cm.ScalarMappable(norm=norm, cmap=cmap)
+        mappable = mpl.cm.ScalarMappable(norm=norm, cmap=cmap)
         mappable.set_array(values)
         if show_colorbar:
             cb = plt.colorbar(mappable, cax=set_colorbar(ax, inset_dict), ax=ax)
@@ -791,12 +789,12 @@ def _matplotlib_points(
             cb.locator = MaxNLocator(nbins=3, integer=True)
             cb.update_ticks()
 
-        cmap = matplotlib.cm.get_cmap(cmap)
+        cmap = mpl.colormaps[cmap]
         colors = cmap(values)
     # No color (just pick the midpoint of the cmap)
     else:
         # main_debug("drawing points without color passed in args, using midpoint of the cmap")
-        colors = plt.get_cmap(cmap)(0.5)
+        colors = mpl.colormaps[cmap](0.5)
         if geo:
             _geo_projection(ax, points, color=colors, **kwargs)
 
@@ -823,7 +821,7 @@ def _matplotlib_points(
             ax.legend(
                 handles=legend_elements,
                 bbox_to_anchor=(1.04, 1),
-                loc=matplotlib.rcParams["legend.loc"],
+                loc=mpl.rcParams["legend.loc"],
                 ncol=len(unique_labels) // 20 + 1,
                 prop=dict(size=8),
             )
@@ -1486,8 +1484,8 @@ def save_return_show_fig_utils(
     prefix: str,
     save_kwargs: Dict,
     total_panels: int,
-    fig: matplotlib.figure.Figure,
-    axes: matplotlib.axes.Axes,
+    fig: mpl.figure.Figure,
+    axes: mpl.axes.Axes,
     return_all: bool,
     return_all_list: Union[List, Tuple, None],
 ) -> Optional[Tuple]:
@@ -1585,7 +1583,7 @@ def check_colornorm(
     vmin: Union[None, float] = None,
     vmax: Union[None, float] = None,
     vcenter: Union[None, float] = None,
-    norm: Union[None, matplotlib.colors.Normalize] = None,
+    norm: Union[None, mpl.colors.Normalize] = None,
 ):
     """
     When plotting continuous variables, configure a normalizer object for the purposes of mapping the data to varying
@@ -1824,7 +1822,7 @@ def dendrogram(
 
 
 def plot_dendrogram(
-    dendro_ax: matplotlib.axes.Axes,
+    dendro_ax: mpl.axes.Axes,
     adata: AnnData,
     cat_key: str,
     dendrogram_key: Union[None, str] = None,
diff --git a/spateo/tdr/models/utilities/label_utils.py b/spateo/tdr/models/utilities/label_utils.py
index 316c6efe..89e8dbeb 100644
--- a/spateo/tdr/models/utilities/label_utils.py
+++ b/spateo/tdr/models/utilities/label_utils.py
@@ -55,7 +55,7 @@ def add_model_labels(
         # Set raw hex.
         if isinstance(colormap, str):
             if colormap in list(mpl.colormaps()):
-                lscmap = mpl.cm.get_cmap(colormap)
+                lscmap = mpl.colormaps[colormap]
                 raw_hex_list = [mpl.colors.to_hex(lscmap(i)) for i in np.linspace(0, 1, len(cu_arr))]
                 for label, color in zip(cu_arr, raw_hex_list):
                     raw_labels_hex[raw_labels_hex == label] = color
diff --git a/spateo/tools/CCI_effects_modeling/MuSIC_downstream.py b/spateo/tools/CCI_effects_modeling/MuSIC_downstream.py
index cea821d4..a6b41d0d 100644
--- a/spateo/tools/CCI_effects_modeling/MuSIC_downstream.py
+++ b/spateo/tools/CCI_effects_modeling/MuSIC_downstream.py
@@ -21,7 +21,7 @@
 from typing import List, Literal, Optional, Tuple, Union
 
 import anndata
-import matplotlib
+import matplotlib as mpl
 import matplotlib.pyplot as plt
 import networkx as nx
 import numpy as np
@@ -2537,7 +2537,7 @@ def effect_distribution_density(
 
         if region_lower_bound is not None and region_upper_bound is not None:
             width = region_upper_bound - region_lower_bound
-            region_box = matplotlib.patches.Rectangle(
+            region_box = mpl.patches.Rectangle(
                 (region_lower_bound, ax.get_ylim()[0]),
                 width,
                 ax.get_ylim()[1] - ax.get_ylim()[0],
@@ -2547,7 +2547,7 @@ def effect_distribution_density(
                 alpha=0.2,
             )
             ax.add_patch(region_box)
-            region_box_legend = matplotlib.patches.Patch(color="#1CE6FF", alpha=0.2, label=region_label)
+            region_box_legend = mpl.patches.Patch(color="#1CE6FF", alpha=0.2, label=region_label)
             handles, labels = ax.get_legend_handles_labels()
             handles.append(region_box_legend)
             labels.append(region_label)
@@ -3049,14 +3049,14 @@ def visualize_effect_specificity(
                 n = 6
             figsize = (n, m)
         fig, ax = plt.subplots(nrows=1, ncols=1, figsize=figsize)
-        cmap = plt.cm.get_cmap(cmap)
+        cmap = mpl.colormaps[cmap]
 
         # Center colormap at 0 for heatmap:
         if plot_type == "heatmap":
             max_distance = max(abs(df.max().max()), abs(df.min().min()))
             norm = plt.Normalize(-max_distance, max_distance)
             colors = cmap(norm(df))
-            custom_cmap = matplotlib.colors.LinearSegmentedColormap.from_list("custom", colors)
+            custom_cmap = mpl.colors.LinearSegmentedColormap.from_list("custom", colors)
         else:
             max_distance = max(abs(df["log2FC"].max()), abs(df["log2FC"].min()))
 
@@ -4152,7 +4152,7 @@ def cell_type_specific_interactions(
         else:
             group_labels = [idx.split("-")[1] for idx in df.index]
 
-        target_colors = plt.cm.get_cmap("tab20").colors
+        target_colors = mpl.colormaps["tab20"].colors
         if group_y_cell_type:
             color_mapping = {
                 annotation: target_colors[i % len(target_colors)] for i, annotation in enumerate(set(cell_types))
@@ -4251,7 +4251,7 @@ def cell_type_specific_interactions(
             rem_interactions = [i for i in interaction_subset if i in df.columns]
             fig, axes = plt.subplots(nrows=len(rem_interactions), ncols=1, figsize=figsize)
             fig.subplots_adjust(hspace=0.4)
-            colormap = plt.cm.get_cmap(cmap)
+            colormap = mpl.colormaps[cmap]
             # Determine the order of the plot based on averaging over the chosen interactions (if there is more than
             # one):
             df_sub = df[rem_interactions]
@@ -4300,7 +4300,7 @@ def cell_type_specific_interactions(
                 vmin = 0
                 vmax = 1 if normalize else df[interaction_subset].max().values
 
-                norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax)
+                norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
                 colors = [colormap(norm(val)) for val in df[interaction_subset].values]
                 sns.barplot(
                     x=df[interaction_subset].index,
@@ -4325,7 +4325,7 @@ def cell_type_specific_interactions(
 
                     vmin = 0
                     vmax = 1 if normalize else interaction_series.max()
-                    norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax)
+                    norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
                     colors = [colormap(norm(val)) for val in interaction_series]
                     sns.barplot(
                         x=interaction_series.index,
@@ -4609,7 +4609,7 @@ def cell_type_interaction_fold_change(
             figsize = (n, m)
         fig, ax = plt.subplots(nrows=1, ncols=1, figsize=figsize)
 
-        cmap = plt.cm.get_cmap(cmap)
+        cmap = mpl.colormaps[cmap]
         # Center colormap at 0:
         max_distance = max(abs(results["Fold Change"]).max(), abs(results["Fold Change"]).min())
         max_pos = results["Fold Change"].max()
diff --git a/win-requirements.txt b/win-requirements.txt
index 72c6bb85..ce8615f9 100755
--- a/win-requirements.txt
+++ b/win-requirements.txt
@@ -12,7 +12,7 @@ kneed>=0.7.0
 kornia>=0.6.4
 loompy>=3.0.5
 mapclassify>=2.4.2
-matplotlib<=3.5.3
+matplotlib>=3.7.5
 nbconvert
 networkx>=2.6.3
 nudged>=0.3.1