Florian Rosenberger et al., 2023 - in revision

Spatial single-cell mass spectrometry defines zonation of the hepatocyte proteome

Abstract

Single-cell proteomics by mass spectrometry (MS) is emerging as a powerful and unbiased method for the characterization of biological heterogeneity. So far, it has been limited to cultured cells, whereas an expansion of the method to complex tissues would greatly enhance biological insights. Here we describe single-cell Deep Visual Proteomics (scDVP), a technology that integrates high-content imaging, laser microdissection and multiplexed MS. scDVP resolves the context-dependent, spatial proteome of murine hepatocytes at a current depth of 1,700 proteins from a slice of a cell. Half of the proteome was differentially regulated in a spatial manner, with protein levels changing dramatically in proximity to the central vein. We applied machine learning to proteome classes and images, which subsequently inferred the spatial proteome from imaging data alone. scDVP is applicable to healthy and diseased tissues and complements other spatial proteomics or spatial omics technologies.

Table of contents

1. Data repository
2. Results
3. R Scripts
4. GitHub Notes

Data repository

Processed mass spectrometry raw data and other input files have been saved in the following folders:

• Data and metadata

Results

Figures and result dataframes are saved in the output folder.

• Results - Figures
• Results - Tables

R scripts

Run "_Top_code.R" to execute the entire R code.

Data wrangling scripts

• Header file
• Filtering criteria
• Prediction proteome calculation
• Clustered tables

Scripts for main figures

Figure 2, Depth of single shape proteomes and estimation of nuclear compartment

• Figure 2a, Abundance range and transcription factors
• Figure 2b, Intensity of top four histones

Figure 3, Single shape proteomes are accurate descriptors of zonated hepatocytes

• Figure 3a, PCA with distance metric overlay
• Figure 3b, Distance metric versus PC1
• Figure 3c, Global heatmap of 70% complete proteome
• Figure 3d, Expression by location
• Figure 3e, Relative expression of top-10 differential proteins
• Figure 3f, Spatial Gene Set Enrichment Analysis
• Figure 3g, Subcellular compartments
• Figure 3h, OXPHOS and mitochondrial fatty acid metabolism
• Figure 3i/j, Urea cycle and peroxisomal fatty acid metabolism

Figure 4, Combining imaging and proteome data for a machine-learned model

• Figure 4a, Pseudo-FACS plot
• Figure 4b, Proteome bin versus staining intensity
• Figure 4e, Proteome prediction of an unseen section
• Figure 4f, Proteome prediction on m4A

Scripts for supplementary figures

Supplementary Figure S1 and S2: Five-shape proteomes

• Figure S1 and S2

Supplementary Figure S3: Performance overview of single shape proteomes

• Figure S3a, Labelling efficiency
• Figure S3b, Area of hepatocytes
• Figure S3c, Protein IDs per biological replicate
• Figure S3d, Microdissected area versus protein IDs
• Figure S3e, Data completeness versus intensity
• Figure S3f, Normalization versus CVs

Supplementary Figure S4: Dimensionality reduction of single shape data

• Figure S4a, PCA with Ass1 expression levels
• Figure S4b, PCA with Cyp2e1 expression levels
• Figure S4c, Distance metric versus PC12
• Figure S4d, PCA with loadings
• Figure S4e, PCA including endothelial cells

Supplementary Figure S5: Concatentation of shapes

• Figure S5, Reductive PCA

Supplementary Figure S6: Normality checks of single shape data

• Figure S5a, Volcano plot
• Figure S5b, Shapiro-Wilk-Test
• Figure S5d, Relative expression of bottom-10 differential proteins

Supplementart Figure S7

• Figure S7a-c, Comparison to RNAseq
• Figure S7d-g, Comparison to FACS-based Proteomics

Supplementary Figure S8: Additional data on subcellular localisation

• Figure S8, Linear models

Supplementart Figure S9

• Figure S9, Machine learning

GitHub Notes

Clone Repository

Navigate to your local GitHub folder and enter:

>git clone https://github.com/MannLabs/single-cell-DVP.git

Transferring local copy to GitHUb

Update current status:

#git pull https://github.com/MannLabs/single-cell-DVP.git

Push changes by indicating the date of change and editor (e.g.: Florian Rosenberger on Feb 20 e.g. 20230220_FR)

1. Get the path of you local GitHub folder

P:\03_Experiments\24_Borderline_Project\18_Github\Borderline_Manuscript>git init

2. Add files and commit changes:

>git add .

>git commit -m "20230220_Editor"

>git remote -v

>git push origin main