MALDIpy is a Python package for analyzing and visualizing MALDI-MS imaging mass spectrometry data (especially designed for spatially resolved metabolomics) at the single-cell level (each “cell” refers to a 10-µm pixel metabolome).

Its function includes: metabolite feature visualization, Scanpy-based single-cell analysis with UMAP clustering, projection of cluster annotations, integrative multi-sample visualization and analysis.

To install MALDIpy, run it in a terminal

pip install MALDIpy

(1) Analyze one single dataset with high efficiency -- all analysis can be done in 5-10 minutes on a labtop: https://github.com/TheHumphreysLab/MALDIpy/blob/main/vignette/kidney_cortex.ipynb

(2) Integrative analysis of multiple MALDI-MS datasets: https://github.com/TheHumphreysLab/MALDIpy/blob/main/vignette/kidney_integration.ipynb

(1) Create a MALDIpy object with msi_data.

maldi_obj = msi_data(raw_file, scale=10)

(2) Visualize any metabolite of interest in a MALDIpy object with plt.

maldi_obj.plt(mz=741.530654593237, figsize = (6,5), smooth=False, pos = 'lower left', remove_hs = True, cmap = "magma_r")

(3) MALDIpy to AnnData conversion with to_adata.

adata = maldi_obj.to_adata(add_meta=True, csv_file=raw_file)

(4) Efficient single-cell quality control, dimension reduction and clustering with single_cell, including single_cell.maldifilter, single_cell.maldi_norm and single_cell.maldi_clustering.
single_cell.maldi_clustering includes Harmony-based batch effect correction when processing multiple tissue sections.

(5) Project single-cell cluster annotation onto the tissue section with projection.

MALDIpy.projection.umap_projection(adata, file_name=raw_file,pltcmap=adata.uns['leiden_colors'],
                                   figtitle='Leiden Cluster Projection',figdpi=150, fig_size=(4,4),add_scalebar=True)

(6) Core plotting functions included in featureplot.
(6.1) Plot a feature with customized colormap with plot1feature and plot a region of interest with the argument subset.

cmap_1 = mcolors.LinearSegmentedColormap.from_list('name1',["black", "lime"], N=256)
fig=MALDIpy.featureplot.plot1feature(tissue_obj, mz_use, cmap = cmap_1, max_num=41000, min_num=21000, figsize = (4.5,5))
fig=MALDIpy.featureplot.plot1feature_subset(tissue_obj,mz_use,cmap = cmap_1, max_num=41000, min_num=21000, figsize = (5,2.9),subset=[95,185,35,175])

(6.2) Plot two features at the same time with customized colormap with plot2features and plot a region of interest with the argument subset.

cmap_1 = mcolors.LinearSegmentedColormap.from_list('name1',["black", "lime"], N=256)
cmap_2 = mcolors.LinearSegmentedColormap.from_list('name2',["black", "magenta"], N=256) 
fig=MALDIpy.featureplot.plot2features(tissue_obj, feats = [mz_use1,mz_use2], cmap=[cmap_1,cmap_2],
                          max_num_1=41000, min_num_1=21000, max_num_2=50000, min_num_2=25000)
fig=MALDIpy.featureplot.plot2features_subset(tissue_obj, feats = [mz_use1,mz_use2], cmap=[cmap_1,cmap_2],
                          max_num_1=41000, min_num_1=21000, max_num_2=50000, min_num_2=25000, subset=[95,185,35,175])

(7) Project your cell cluster of interest onto a group of tissue sections with projection.project_cluster_in_groups.
First, extract X/Y coordinates from the anndata and add them to adata.obs with projection.add_coords

adata = MALDIpy.projection.add_coords(adata)

Then, visualize the cluster of interest across multiple samples.

group=['sample1','sample2','sample3','sample4','sample5','sample6']
fig = MALDIpy.projection.project_cluster_in_groups(adata, cluster_id='1', cluster_obs_name='leiden', 
                                group_list=group, group_obs_name='sample_id', cmap='Reds')

(1) MALDI-MS (Matrix-assisted laser desorption/ionization mass spectrometry)
(2) METASPACE - cloud platform for spatial metabolomics
(3) Scanpy - Single-Cell Analysis in Python
(4) Harmony - integrating multiple high-dimensional datasets

We recommend cite the following two related manuscripts for the use of MALDIpy:

Cell Metabolism 2024 (PMID: 38513647):

Li, Haikuo, et al. "Transcriptomic, epigenomic, and spatial metabolomic cell profiling redefines regional human kidney anatomy." Cell metabolism 36.5 (2024): 1105-1125.

Data in Brief 2024 (PMID: 38708307):

Li, Haikuo, and Benjamin D. Humphreys. "Spatially resolved metabolomic dataset of distinct human kidney anatomic regions." Data in Brief 54 (2024): 110431.

Adobe Firefly AI painting with command a robot analyzing tissue section metabolites with imaging mass spectrometry.

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