Mardiyyah/TAPT_data_V2_split · Datasets at Hugging Face

PMCID stringclasses 24 values	Title stringclasses 24 values	Sentences stringlengths 2 40.7k
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	The demultiplexing of the raw data was performed using CellRanger software (10x – version 3.1.0; cellranger mkfastq which wraps Illumina’s bcl2fastq).
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	The reads obtained from the demultiplexing were used as the input for ‘cellranger count’ (CellRanger software), which aligns the reads to the mouse reference genome (mm10) using STAR and collapses to unique molecular identifier (UMI) counts.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	The result is a large digital expression matrix with cell barcodes as rows and gene identities as columns.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	To remove ambient RNA, the FastCAR R package (v0.1.0) with a contamination chance cutoff of 0.05 was run on the samples separately before merging them.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	The UMI cut off was determined individually for the different samples, using the CellRanger web_summary output plot (see GitHub).
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	The Scater R package (v1.14.6) was used for the preprocessing of the data.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	The workflow to identify the outliers, based on 3 metrics (library size, number of expressed genes and mitochondrial proportion) described by the Marioni lab (Lun et al., 2016) was followed.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	As a first step cells with a value x median absolute deviation (MADs) higher or lower than the median value for each metric were removed.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	This value was determined individually for the different datasets (see github).
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	Secondly, the runPCA function (default parameters) of the Scater R package was used to generate a principal component analysis (PCA) plot.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	The outliers in this PCA plot were identified by the R package mvoutlier.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	By creating the Seurat object, genes that didn’t have an expression in at least 3 cells were removed.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	To normalize, scale and detecting the highly variable genes, the R package SCTransform (v0.2.1) was used.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	If batch correction (on sample level) was needed, the NormalizeData (log2 transformation), FindVariableFeatures and ScaleData functions of the Seurat R package (v3.1.2) were used in combination with the Harmony R package (v1.0).
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	The Seurat pipeline was followed to find the clusters and create the UMAP plots.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	The number of principal components used for the clustering and the resolution were determined individually for the different datasets (see GitHub).
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	On these initial UMAP plots we did multiple rounds of cleaning by removing proliferating and contaminating (e.g. doublets) cells.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	For non CITE-seq datasets the count data for the clean cells acquired by the previous steps were further processed with the scVI model (scvi Python package v0.6.7) (Lopez et al., 2018).
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	Datasets including Cite-seq samples were further processed with the TotalVI model (Gayoso et al., 2021).
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	The workflows described on scvi-tools.org were followed to generate new UMAPs, DEGs and DEPs.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	This information was further processed with the pheatmap R package (v1.0.12) to create heatmaps using the normalized values (denoised genes) calculated in the scVI/TotalVI workflow.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	The plots showing the expression of certain genes or proteins are created with the ggplot2 R package (v3.2.1) with a quantile cut off of 0.01.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	For mouse all the ABs from the whitelist (181 ABs) were loaded into TotalVI, while for the other species only the added ABs were loaded into TotalVI.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	For the ‘human liver-pool of techniques and patients’ we noticed that the batch correction (between samples) faced difficulties for the hepatocytes and stellate cells as the cells all originated from snRNA-Seq samples, while the other cell types originated from both snRNA-seq and scRNA-seq samples.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	To overcome this issue we randomly allocated 30% of the hepatocytes to scRNA-seq samples which were not CITE-seq samples.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	We did the same for 30% of the stellate cells.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	Heatmaps were made by scaling the normalized values (denoised values; calculated in the scVI/TotalVI workflow) using the scale_quantile function of the SCORPIUS R package (v1.0.7) and the pheatmap R package (v1.0.12).
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	The plots showing the expression of certain genes or proteins were created based on the normalized values (denoised values) using a quantile cutoff of 0.99 and via either the ggplot2 R package (v3.2.1) or the scanpy.pl.umap function of the Scanpy Python package (v1.5.1).
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	To find the conserved human and mouse KC markers we started by identifying the human KC markers.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	We mapped the annotation of the human myeloid UMAP on the human pool of techniques/patients UMAP to identify the real KCs in this last UMAP.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	The real KCs were identified as the top part of the mac cluster.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	Using this new annotation we then calculated the DE genes and DE proteins for each cluster.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	Some genes are listed as marker for multiple clusters, only for the cluster where the gene had the highest score (raw_normalized_mean1/raw_normalized_mean2lfc_mean), the gene was kept as marker.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	This way we found 110 potential human KC markers.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	We then created a heatmap of these 110 genes (using denoised gene values scaled between 0 and 1) and filtered this heatmap by removing the genes where the scaled normalized value was higher than 0.50 in more than 30% of the cells of a certain cell type other than KCs.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	Except for the macs, we only removed a gene when it had a scaled normalized value higher than 0.50 in more than 70% of the macs.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	After this filtering we ended up with 36 human KC markers.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	Next we converted these human gene symbols into MGI IDs via the BioMart tool on the HGNC website (https://biomart.genenames.org/martform/#!/default/HGNC?datasets=hgnc_gene_mart).
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	We found a MGI ID for 30 genes.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	We then converted these MGI IDs into mouse gene symbols via the MGI webtool (http://www.informatics.jax.org/batch/).
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	To identify the mouse KC markers we similarly mapped the annotation of the mouse myeloid UMAP on the mouse pool of techniques UMAP to identify the real KCs in this last UMAP.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	The real KCs matched with the mac cluster.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	Similarly as in human, the DE genes for each cluster was calculated and genes listed as marker for multiple clusters were dealt with in a similar way.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	This way we found 264 potential mouse KC markers.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	We then removed the genes that had a score (raw_normalized_mean1/raw_normalized_mean2lfc_mean) lower than 10 and ended up with 214 genes.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	We then created a heatmap of these 214 genes (using denoised gene values scaled between 0 and 1) and filtered this heatmap by removing the genes where the scaled normalized value was higher than 0.50 in more than 30% of the cells of a certain cell type other than KCs.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	After this filtering we ended up with 68 mouse KC markers.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	Next we converted these mouse gene symbols into MGI IDs via the MGI webtool (http://www.informatics.jax.org/batch/).
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	We then converted these MGI IDs into human gene symbols via the BioMart tool on the HGNC website (https://biomart.genenames.org/martform/#!/default/HGNC?datasets=hgnc_gene_mart) and ended up with 60 genes.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	At this point we found 30 human KC markers and 60 mouse KC markers.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	In a next step, we only kept the human KC markers that we identified as a Highly Variable Gene (HVG) in the mouse pool of techniques UMAP (20 genes) and the mouse KC markers that were identified as HVGs in the human pool of the techniques UMAP (30 genes).
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	We next put these 20 mouse KC markers in SingleCellSignatureExplorer (Pont et al., 2019) to see where these genes are enriched in the mouse pool of techniques UMAP.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	In order to only get an enrichment in the KCs we decided to only use top 10 mouse KC markers (ordered on score), together with Slc40a1 and Hmox1.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	We then started to add the top human KC markers as long as we keep the enrichment solely in the KCs.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	This way we ended up with final list of 15 human-mouse conserved KC markers.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	We next converted these KC markers into the monkey, pig, chicken or zebrafish orthologs by looking up the human gene symbol on NCBI (https://www.ncbi.nlm.nih.gov/search/) and checking if there is an ortholog of the species of interest listed under the ‘Ortholog’ tab.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	The found orthologs were then used as input for the SingleCellSignatureExplorer tool.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	The protein normalized values (denoised values; calculated in the TotalVI workflow) were converted into an FCS file using the write.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	FCS function of the flowCore R package (v1.50.0).
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	We first removed per sample all spots that were clear outliers compared to the location of the tissue.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	Each sample was then normalized individually using the SCTransform function of the Seurat R package (v3.2.3) with default parameters.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	All samples were then merged with the merge function of the Seurat R package (v3.2.3) with default parameters.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	Next, we determined the HVGs, created a PCA plot, performed clustering and created an UMAP plot as described in the spatial workflow available on the Seurat website (https://satijalab.org/seurat/articles/spatial_vignette.html).
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	Clusters which showed high mitochondrial gene expression were removed.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	Spots located at the darker parts of the tissue were also removed as these parts are considered to be dead tissue or of bad quality.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	For modelling the cell type composition and zonation, spatial CITE-seq and transcriptomics data were analyzed using probabilistic graphical models, similar to what is used in tools such as cell2location and scVI.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	In brief, transcriptomics data was modelled as a NegativeBinomial distribution, parameterized with a mean and dispersion , the latter optimized as a free parameter for each gene.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	Visium Highly Multiplexed Protein data was modelled as a mixture of NegativeBinomials, with a and and a shared dispersion .
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	The actual foreground/background signal within a modality was modelled as a that depends on the latent space, and which is multiplied with the empirical library size to get .
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	For Visium Highly Multiplexed Protein, was modelled as a latent variable specific for each gene.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	for Visium Highly Multiplexed Protein and for RNA-seq were modelled as deterministic functions depending on the use case as described in the following paragraphs.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	The posterior of the probabilistic graphical model was inferred using black-box variational inference (Ranganath et al., 2013), in which the variational distribution was specified as a diagonal Normal distribution, transformed into the correct domain using transforms (, , ).
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	Free parameters within this model were optimized using gradient descent, with the ELBO as loss function and Adam as optimizer as implemented in Pytorch (Paszke et al., 2019) (pytorch.org).
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	We used a learning rate of 0.01 for variational parameters, and 0.001 for parameters of the amortization functions.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	To calculate the average expression of each gene within a cell type, we used a linear model in which both and were modelled as a latent variable specific for each gene and cell type.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	The for nuclei were multiplied with a gene-specific correction factor (optimized as a latent variable) that corrected for differences between scRNA-seq and snRNA-seq.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	Given that spatial transcriptomics data sequences the whole cell, the uncorrected values were used for spatial deconvolution.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	To infer the proportions of each cell type within a spot, we used a model in which the gene expression is modelled as a linear combination of cell type proportions and average expression in each cell type: For we adapted the values from the reference, but included- A capture bias per gene, which corrects for technical and biological differences between spatial and sc/sn-RNA-seq.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	The capture bias was modelled as a latent variable with prior - A red blood cell cell type, which was not included in the reference dataset but nonetheless had a dominant presence in the spatial data.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	The of this cell type was set to zero for all genes except Hbb-bt, Hbb-bs, Hba-a1, Hba-a2 for mouse and HBB, HBA1, HBA2 for human, which were modelled as free parameters.- Similarly, the expression of complement factors (C3, C2, C4B/C4b) within hepatocytes was modelled as free parameters. -
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	A capture bias per gene, which corrects for technical and biological differences between spatial and sc/sn-RNA-seq.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	The capture bias was modelled as a latent variable with prior - A red blood cell cell type, which was not included in the reference dataset but nonetheless had a dominant presence in the spatial data.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	The of this cell type was set to zero for all genes except Hbb-bt, Hbb-bs, Hba-a1, Hba-a2 for mouse and HBB, HBA1, HBA2 for human, which were modelled as free parameters. -
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	Similarly, the expression of complement factors (C3, C2, C4B/C4b) within hepatocytes was modelled as free parameters.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	A background signal shared for all spots was also modelled as follows: With a latent variable specific to each spot and a latent variable specific to each gene.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	A likelihood ratio test was used to assess whether a cell type was significantly present in a spot.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	Specifically, if is the gene expression of all genes at a particular spot, we used Monte Carlo samples from the posterior to estimate:P(x\|νcelltype)P(x\|νcelltype=0) A cell type was deemed significantly present if the log-likelihood was higher than 10.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	The zonation of spots was modelled as a univariate latent variable specific to each spot.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	This latent variable influenced the gene expression using a spline function by using a gaussian basis function () with 10 knots at uniform fixed positions.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	The coefficients of this spline were modelled as a latent variable specific for each gene, with prior a Gaussian random walk distribution, and the step .
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	was determined empirically as 2 times the standard deviation of the log1p transformed expression values in the whole dataset.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	The variational parameters of the zonation and were not optimized directly but were estimated using an amortization function.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	This amortization function used the count matrix as input, and estimated the variational parameters using the following layers: Linear (with 100 output dimensions), BatchNorm, ReLU, Linear (again with 100 output dimensions), ReLU, and a final Linear layer.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	This amortization function was used to transfer the zonation onto a different dataset, i.e., 1) to transfer the zonation trained on mouse spatial transcriptomics onto mouse Visium highly multiplexed protein and 2) to transfer the zonation trained on human low steatosis (<10%) onto human high steatosis (>30%).
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	To determine the differential abundance of a cell type across zonation, the significant presence of a cell type within a spot was modelled using a spline function with the zonation of a cell type as input.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	The coefficients of this spline function were modelled as a latent variable with the step size .
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	To determine differences in abundance between patients with high and low steatosis, we first modelled the zonation on human data on patients with steatosis < 10%.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	Potential interaction effects between zonation and steatosis status were then modelled using a spline function as before, but with a separate set of coefficients for both high and low steatosis.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	A likelihood ratio test was then used to determine whether this interaction was present significantly, by comparing the likelihood of this model with a model with shared coefficients.
PMC8809252	Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.	To analyze cell-cell communication in the hepatic mac niches, we applied Differential NicheNet, which is an extension of the default NicheNet pipeline to compare cell-cell interactions between different niches and better predict niche-specific ligand-receptor (L-R) pairs.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

The demultiplexing of the raw data was performed using CellRanger software (10x – version 3.1.0; cellranger mkfastq which wraps Illumina’s bcl2fastq).

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

The reads obtained from the demultiplexing were used as the input for ‘cellranger count’ (CellRanger software), which aligns the reads to the mouse reference genome (mm10) using STAR and collapses to unique molecular identifier (UMI) counts.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

The result is a large digital expression matrix with cell barcodes as rows and gene identities as columns.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

To remove ambient RNA, the FastCAR R package (v0.1.0) with a contamination chance cutoff of 0.05 was run on the samples separately before merging them.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

The UMI cut off was determined individually for the different samples, using the CellRanger web_summary output plot (see GitHub).

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

The Scater R package (v1.14.6) was used for the preprocessing of the data.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

The workflow to identify the outliers, based on 3 metrics (library size, number of expressed genes and mitochondrial proportion) described by the Marioni lab (Lun et al., 2016) was followed.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

As a first step cells with a value x median absolute deviation (MADs) higher or lower than the median value for each metric were removed.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

This value was determined individually for the different datasets (see github).

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

Secondly, the runPCA function (default parameters) of the Scater R package was used to generate a principal component analysis (PCA) plot.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

The outliers in this PCA plot were identified by the R package mvoutlier.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

By creating the Seurat object, genes that didn’t have an expression in at least 3 cells were removed.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

To normalize, scale and detecting the highly variable genes, the R package SCTransform (v0.2.1) was used.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

If batch correction (on sample level) was needed, the NormalizeData (log2 transformation), FindVariableFeatures and ScaleData functions of the Seurat R package (v3.1.2) were used in combination with the Harmony R package (v1.0).

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

The Seurat pipeline was followed to find the clusters and create the UMAP plots.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

The number of principal components used for the clustering and the resolution were determined individually for the different datasets (see GitHub).

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

On these initial UMAP plots we did multiple rounds of cleaning by removing proliferating and contaminating (e.g. doublets) cells.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

For non CITE-seq datasets the count data for the clean cells acquired by the previous steps were further processed with the scVI model (scvi Python package v0.6.7) (Lopez et al., 2018).

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

Datasets including Cite-seq samples were further processed with the TotalVI model (Gayoso et al., 2021).

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

The workflows described on scvi-tools.org were followed to generate new UMAPs, DEGs and DEPs.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

This information was further processed with the pheatmap R package (v1.0.12) to create heatmaps using the normalized values (denoised genes) calculated in the scVI/TotalVI workflow.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

The plots showing the expression of certain genes or proteins are created with the ggplot2 R package (v3.2.1) with a quantile cut off of 0.01.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

For mouse all the ABs from the whitelist (181 ABs) were loaded into TotalVI, while for the other species only the added ABs were loaded into TotalVI.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

For the ‘human liver-pool of techniques and patients’ we noticed that the batch correction (between samples) faced difficulties for the hepatocytes and stellate cells as the cells all originated from snRNA-Seq samples, while the other cell types originated from both snRNA-seq and scRNA-seq samples.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

To overcome this issue we randomly allocated 30% of the hepatocytes to scRNA-seq samples which were not CITE-seq samples.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

We did the same for 30% of the stellate cells.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

Heatmaps were made by scaling the normalized values (denoised values; calculated in the scVI/TotalVI workflow) using the scale_quantile function of the SCORPIUS R package (v1.0.7) and the pheatmap R package (v1.0.12).

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

The plots showing the expression of certain genes or proteins were created based on the normalized values (denoised values) using a quantile cutoff of 0.99 and via either the ggplot2 R package (v3.2.1) or the scanpy.pl.umap function of the Scanpy Python package (v1.5.1).

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

To find the conserved human and mouse KC markers we started by identifying the human KC markers.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

We mapped the annotation of the human myeloid UMAP on the human pool of techniques/patients UMAP to identify the real KCs in this last UMAP.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

The real KCs were identified as the top part of the mac cluster.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

Using this new annotation we then calculated the DE genes and DE proteins for each cluster.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

Some genes are listed as marker for multiple clusters, only for the cluster where the gene had the highest score (raw_normalized_mean1/raw_normalized_mean2lfc_mean), the gene was kept as marker.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

This way we found 110 potential human KC markers.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

We then created a heatmap of these 110 genes (using denoised gene values scaled between 0 and 1) and filtered this heatmap by removing the genes where the scaled normalized value was higher than 0.50 in more than 30% of the cells of a certain cell type other than KCs.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

Except for the macs, we only removed a gene when it had a scaled normalized value higher than 0.50 in more than 70% of the macs.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

After this filtering we ended up with 36 human KC markers.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

Next we converted these human gene symbols into MGI IDs via the BioMart tool on the HGNC website (https://biomart.genenames.org/martform/#!/default/HGNC?datasets=hgnc_gene_mart).

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

We found a MGI ID for 30 genes.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

We then converted these MGI IDs into mouse gene symbols via the MGI webtool (http://www.informatics.jax.org/batch/).

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

To identify the mouse KC markers we similarly mapped the annotation of the mouse myeloid UMAP on the mouse pool of techniques UMAP to identify the real KCs in this last UMAP.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

The real KCs matched with the mac cluster.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

Similarly as in human, the DE genes for each cluster was calculated and genes listed as marker for multiple clusters were dealt with in a similar way.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

This way we found 264 potential mouse KC markers.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

We then removed the genes that had a score (raw_normalized_mean1/raw_normalized_mean2lfc_mean) lower than 10 and ended up with 214 genes.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

We then created a heatmap of these 214 genes (using denoised gene values scaled between 0 and 1) and filtered this heatmap by removing the genes where the scaled normalized value was higher than 0.50 in more than 30% of the cells of a certain cell type other than KCs.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

After this filtering we ended up with 68 mouse KC markers.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

Next we converted these mouse gene symbols into MGI IDs via the MGI webtool (http://www.informatics.jax.org/batch/).

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

We then converted these MGI IDs into human gene symbols via the BioMart tool on the HGNC website (https://biomart.genenames.org/martform/#!/default/HGNC?datasets=hgnc_gene_mart) and ended up with 60 genes.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

At this point we found 30 human KC markers and 60 mouse KC markers.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

In a next step, we only kept the human KC markers that we identified as a Highly Variable Gene (HVG) in the mouse pool of techniques UMAP (20 genes) and the mouse KC markers that were identified as HVGs in the human pool of the techniques UMAP (30 genes).

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

We next put these 20 mouse KC markers in SingleCellSignatureExplorer (Pont et al., 2019) to see where these genes are enriched in the mouse pool of techniques UMAP.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

In order to only get an enrichment in the KCs we decided to only use top 10 mouse KC markers (ordered on score), together with Slc40a1 and Hmox1.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

We then started to add the top human KC markers as long as we keep the enrichment solely in the KCs.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

This way we ended up with final list of 15 human-mouse conserved KC markers.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

We next converted these KC markers into the monkey, pig, chicken or zebrafish orthologs by looking up the human gene symbol on NCBI (https://www.ncbi.nlm.nih.gov/search/) and checking if there is an ortholog of the species of interest listed under the ‘Ortholog’ tab.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

The found orthologs were then used as input for the SingleCellSignatureExplorer tool.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

The protein normalized values (denoised values; calculated in the TotalVI workflow) were converted into an FCS file using the write.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

FCS function of the flowCore R package (v1.50.0).

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

We first removed per sample all spots that were clear outliers compared to the location of the tissue.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

Each sample was then normalized individually using the SCTransform function of the Seurat R package (v3.2.3) with default parameters.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

All samples were then merged with the merge function of the Seurat R package (v3.2.3) with default parameters.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

Next, we determined the HVGs, created a PCA plot, performed clustering and created an UMAP plot as described in the spatial workflow available on the Seurat website (https://satijalab.org/seurat/articles/spatial_vignette.html).

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

Clusters which showed high mitochondrial gene expression were removed.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

Spots located at the darker parts of the tissue were also removed as these parts are considered to be dead tissue or of bad quality.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

For modelling the cell type composition and zonation, spatial CITE-seq and transcriptomics data were analyzed using probabilistic graphical models, similar to what is used in tools such as cell2location and scVI.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

In brief, transcriptomics data was modelled as a NegativeBinomial distribution, parameterized with a mean and dispersion , the latter optimized as a free parameter for each gene.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

Visium Highly Multiplexed Protein data was modelled as a mixture of NegativeBinomials, with a and and a shared dispersion .

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

The actual foreground/background signal within a modality was modelled as a that depends on the latent space, and which is multiplied with the empirical library size to get .

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

For Visium Highly Multiplexed Protein, was modelled as a latent variable specific for each gene.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

for Visium Highly Multiplexed Protein and for RNA-seq were modelled as deterministic functions depending on the use case as described in the following paragraphs.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

The posterior of the probabilistic graphical model was inferred using black-box variational inference (Ranganath et al., 2013), in which the variational distribution was specified as a diagonal Normal distribution, transformed into the correct domain using transforms (, , ).

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

Free parameters within this model were optimized using gradient descent, with the ELBO as loss function and Adam as optimizer as implemented in Pytorch (Paszke et al., 2019) (pytorch.org).

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

We used a learning rate of 0.01 for variational parameters, and 0.001 for parameters of the amortization functions.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

To calculate the average expression of each gene within a cell type, we used a linear model in which both and were modelled as a latent variable specific for each gene and cell type.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

The for nuclei were multiplied with a gene-specific correction factor (optimized as a latent variable) that corrected for differences between scRNA-seq and snRNA-seq.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

Given that spatial transcriptomics data sequences the whole cell, the uncorrected values were used for spatial deconvolution.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

To infer the proportions of each cell type within a spot, we used a model in which the gene expression is modelled as a linear combination of cell type proportions and average expression in each cell type: For we adapted the values from the reference, but included- A capture bias per gene, which corrects for technical and biological differences between spatial and sc/sn-RNA-seq.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

The capture bias was modelled as a latent variable with prior - A red blood cell cell type, which was not included in the reference dataset but nonetheless had a dominant presence in the spatial data.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

The of this cell type was set to zero for all genes except Hbb-bt, Hbb-bs, Hba-a1, Hba-a2 for mouse and HBB, HBA1, HBA2 for human, which were modelled as free parameters.- Similarly, the expression of complement factors (C3, C2, C4B/C4b) within hepatocytes was modelled as free parameters. -

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

A capture bias per gene, which corrects for technical and biological differences between spatial and sc/sn-RNA-seq.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

The capture bias was modelled as a latent variable with prior - A red blood cell cell type, which was not included in the reference dataset but nonetheless had a dominant presence in the spatial data.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

The of this cell type was set to zero for all genes except Hbb-bt, Hbb-bs, Hba-a1, Hba-a2 for mouse and HBB, HBA1, HBA2 for human, which were modelled as free parameters. -

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

Similarly, the expression of complement factors (C3, C2, C4B/C4b) within hepatocytes was modelled as free parameters.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

A background signal shared for all spots was also modelled as follows: With a latent variable specific to each spot and a latent variable specific to each gene.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

A likelihood ratio test was used to assess whether a cell type was significantly present in a spot.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

Specifically, if is the gene expression of all genes at a particular spot, we used Monte Carlo samples from the posterior to estimate:P(x|νcelltype)P(x|νcelltype=0) A cell type was deemed significantly present if the log-likelihood was higher than 10.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

The zonation of spots was modelled as a univariate latent variable specific to each spot.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

This latent variable influenced the gene expression using a spline function by using a gaussian basis function () with 10 knots at uniform fixed positions.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

The coefficients of this spline were modelled as a latent variable specific for each gene, with prior a Gaussian random walk distribution, and the step .

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

was determined empirically as 2 times the standard deviation of the log1p transformed expression values in the whole dataset.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

The variational parameters of the zonation and were not optimized directly but were estimated using an amortization function.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

This amortization function used the count matrix as input, and estimated the variational parameters using the following layers: Linear (with 100 output dimensions), BatchNorm, ReLU, Linear (again with 100 output dimensions), ReLU, and a final Linear layer.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

This amortization function was used to transfer the zonation onto a different dataset, i.e., 1) to transfer the zonation trained on mouse spatial transcriptomics onto mouse Visium highly multiplexed protein and 2) to transfer the zonation trained on human low steatosis (<10%) onto human high steatosis (>30%).

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

To determine the differential abundance of a cell type across zonation, the significant presence of a cell type within a spot was modelled using a spline function with the zonation of a cell type as input.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

The coefficients of this spline function were modelled as a latent variable with the step size .

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

To determine differences in abundance between patients with high and low steatosis, we first modelled the zonation on human data on patients with steatosis < 10%.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

Potential interaction effects between zonation and steatosis status were then modelled using a spline function as before, but with a separate set of coefficients for both high and low steatosis.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

A likelihood ratio test was then used to determine whether this interaction was present significantly, by comparing the likelihood of this model with a model with shared coefficients.

PMC8809252

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

To analyze cell-cell communication in the hepatic mac niches, we applied Differential NicheNet, which is an extension of the default NicheNet pipeline to compare cell-cell interactions between different niches and better predict niche-specific ligand-receptor (L-R) pairs.