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# System prepended metadata

title: '22 10 05  Combined single-cell and spatial transcriptomics reveal the molecular, cellular and spatial bone marrow niche organization'

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# 22 10 05  Combined single-cell and spatial transcriptomics reveal the molecular, cellular and spatial bone marrow niche organization

## 1. Introduction
Bone marrow (BM) niches are specialized microenvironments where distinct mesenchymal cells, the vasculature and differentiated haematopoietic cells interact to regulate the maintenance and differentiation of haematopoietic and mesenchymal stem cells (HSCs and MSCs).

- Classically, BM niches have been  studied  using  genetic  approaches  that  involve  labelling  cell  types and ablating candidate niche factors based on the expression of  individual  reporter  genes.
- However, single markers are  used  to  define  cell  types  in  such  approaches,  probably  resulting in the **labelling of heterogeneous populations.**
-  These limitations have resulted in a controversial **debate** about the importance of **distinct cell types** and factors, and their **localization** to sinusoidal, arteriolar or endosteal niches

**Main approach**: We  then  used  spatially  resolved  transcriptomics  in  combination  with  computational  tools  to  allocate  cell  types  to  different  BM  niches,  determine  the  molecular  mediators  of  inter-cellular interactions, and identify the cellular and spatial sources of niche factors.

## 2. Results

### 2.1 Identification and characterization of BM-resident cell types by scRNAseq.
To  capture  both  highly  abundant  and  extremely  rare  BM-resident cells, we performed droplet-based scRNAseq of cells from total mouse BM, followed by progressive depletion of the abundant cell types or enrichment of rare populations from undigested BM or enzymatically digested bones



![](https://i.imgur.com/IboASDs.png)
***Figure 1. a**, Overview of the FACS strategy (left). Five consecutive scRnAseq runs were performed in total. T-distributed stochastic neighbour embedding (t-SnE) of all cells, colour-coded according to the respective experiment on the left (right). **b**, 7,497  cells,  which  formed  32  clusters  corresponding  to  distinct cell types or stages of differentiation. Cell-type annotation was performed based on the expression of marker genes. t-SnE projection of all cells with the clusters colour-coded.*

https://nicheview.shiny.embl.de/

- Major  **haematopoietic  cell  types**,  including  dendritic  cells,  neutrophils,  monocytes,  T  cells  and  distinct  developmental  stages  of  B  cells. Erythroid progenitors that exhibited low expression of the panhaematopoietic  marker  CD45  and  displayed  erythroid  markers  such  as CD71.
- To capture the **non-haematopoietic cells** in depth, we subsequently depleted cells expressing the lineage markers, CD45 or CD71 from non-digested BM and enzymatically digested bone chips. ->

**This is how they come up with the endothelials and mesenchymals in Figure 2.**

- **Endothelial  populations**  com-prised  Sca1  (Ly6a)-expressing  arterial  ECs  and  Emcn-expressing sinusoidal  ECs.
- **Mesenchymal  lineage:** chondrocytes (Acan, Sox9), osteoblasts (Osteocalcin/Bglap, Col1a1) and several less well-described cell types:
    - Three distinct fibroblast-like populations (annotated on localization).
    -  two additional populations that showed a high transcriptomic similarity to previously described SCF–GFP.
    -  Cxcl12–GFP+, Cxcl12-abundant  reticular  (CAR).  These  two  populations  expressed  adipocyte  and  osteo-lineage  genes  differentially (Fig. 2d), despite similar overall transcriptomic profiles. We therefore termed these subpopulations ‘**Adipo-CAR**’ and ‘**Osteo-CAR**’ cells, respectively. 
    -  Ng2-  and  Nestin+

![](https://i.imgur.com/rWw1Lmz.jpg)
***Figure 2.** **a**, Gene expression levels of Sca1 (Ly6a), endomucin (Emcn), nestin (Nes) and ng2 (Cspg4), with the relevant populations colour-coded. MAGIC44 was used for the imputation of drop-out values. **b**, Deep imaging of a BM section immunostained with antibodies against Sca1 and Emcn. Scale bar, 20 μm. **c**, Enrichment of the gene expression signatures of Adipo-CAR cells (left), Osteo-CAR cells (middle) and ng2+ MSCs (right) in previously published transcriptomes of relevant genetically labelled populations. Individual data points represent n= 3 replicates; the bars denote the mean. **d**, Boxplots of the scaled expression levels of selected genes in single cells from the Adipo-CAR (n= 528), Osteo-CAR (n= 57) and osteoblast (n= 53) populations. The mean expression of all genes annotated with the gene ontology (GO) term ‘ossification’ was computed for each cell (right). ,The  endothelial  populations  com-prised  Sca1  (Ly6a)-expressing  arterial  ECs  and  Emcn-expressing sinusoidal  ECs. **e**, Projection of all mesenchymal cell types using PHATE45 with the time derivatives of the gene expression state, as determined by RnA velocity20, highlighted as arrows; n= 1,784 cells **f**, Comparison of the cell-type frequencies obtained through distinct cell isolation methods used for scRnAseq*

> **RNA velocity:** separating the different stages in mRNA transcription and maturation we can get a sense of whether the gene is being upregulated or downregulated in the next 2-3h, which corresponds to 1st derivative of gene expression. For each gene we have a direction of whether it's oing up or down. If we do it for every single gene in the cell, we estimate the "direction" of the cell. This is RNA velocity.
> ![](https://i.imgur.com/J0kbTv8.png)

- The **fibroblast** populations, myofibroblasts and Schwann cells were found to be more **abundant in crushed bones** when compared with flushed bones. 

### 2.2 Spatial allocation of BM-resident cell types by combined single-cell  and  spatial  transcriptomics

**single-cell  transcriptional profiling** provides a powerful tool to characterize the **identity and  molecular  make-up** of  BM  cell  types,  information  regarding  their  **spatial  distribution  is  lost**. 

- -> laser-capture  **microdissection  coupled  with  sequencing  (LCM-seq):** BM sections containing 200–300 cells in  a  single-cell  layer. 
- Marker genes of cell types known  to  be  specific  to  the  respective  niches  and  **compared  their  expression in scRNAseq and corresponding spatial transcriptomics data.**
- marker  gene  expression  of  the  **remaining  12  populations  differed  significantly across niches**.
    - -> To assess the localization of these BM cell  types  to  candidate  niches,  we  estimated  the  frequencies  of  the  cell populations defined by scRNAseq in the spatially resolved transcriptomics  data  using  the  CIBERSORT  algorithm.


![](https://i.imgur.com/HnwXpY9.jpg)
***Figure 3.a,** Schematic of the experimental design. Bone sections (12 µm) were stained for arterioles (Sca1) and sinusoids (Emcn). Areas of approximately 14,500 µm2 surrounding arteries (dark yellow box), sinusoids (yellow box) and the endosteum (blue box), as well as areas with no vessels (grey box) and sub-endosteal areas (dotted blue box) were collected using laser-capture dissection and subjected to RNA-seq. A confocal image is shown for illustrative purposes. Scale bar, 100 µm.**b,** Expression of osteoblast-, sinusoid- and arteriole-specific genes in the scRNAseq data (10x) and spatial transcriptomics from different niches (LCM, LCM-seq data); n, number of cells or niches as indicated on the graph. **c,** Enrichment of population marker genes (Supplementary Table 1) among genes with differential expression between niches (Supplementary Table 2). The P values were derived using a re-sampling-based test; n = 25 cell types are shown; all HSPC subtypes were summarized as a single population. **d,** Schematic outline of the computational data-analysis strategy used. **e,** Estimated abundance of different cell types in microscopically distinct niches. The means of the CIBERSORT estimates are shown; the error bars indicate the standard error of the mean. Arteriolar niches, n = 28; endosteal niches, n = 12; sinusoidal niches, n = 14; non-vascular niches, n = 11; and sub-endosteal niches, n = 11. **f,** Heatmap summarizing the data from e.*

Using LCM-Seq they can relate the previously described populations to known niches.
- osteoblasts  and  chondrocytes  were  found  exclusively  at  the  end-osteal  niches
- a  specific  fibroblast  popula-tion  localized  preferentially  to  the  endosteum -> endosteal  fibroblasts
- Arterial  ECs,  smooth  muscle  cells  and  a  distinct  fibroblast  population  localized  specifically to the arteriolar niches 
- Sinusoidal  ECs  were  found  in the sinusoidal niches but were also present in the (sub)-endosteal niches, in accordance with a widespread web of sinusoids spanning the  entire  BM.
- Adipo-CAR  cells  were  found  predominantly  in  areas  with  high  sinusoidal  occurrence,  in  line  with  their  similarity  to  LepR–Cre  cells  and  the  reported peri-sinusoidal localization of these cell types.
- Previously  undescribed  Osteo-CAR  cells  preferentially  local-ized  to  arteriolar  or  non-vascular  niches,  suggesting  that  the  two  CAR  cell  populations  occupy  distinct  niches. 
- Ng2+  MSCs  could  not  be  unanimously  assigned  to  a  niche,  potentially  due  to  addi-tional  heterogeneity  within  this  population.

### 2.3 Validation  of  cell-type  localization
 **marker  combinations  specific  to  the  individual  populations**  and  performed immunofluorescence imaging of bone sections.
 
 - alka-line  phosphatase  (Alpl)  and  Cxcl12  allows  discrimination  between  Osteo-CAR  (Cxcl12+Alpl+),  Adipo-CAR  cells  (Cxcl12+Alpl−),  and  Cxcl12−Alpl+ cell types, such as osteoblasts, Ng2+ MSCs and arterial ECs.
 - confirm the in situ localization of the Adipo- and Osteo-CAR cells, we performed whole-mount immunofluorescence imaging of the long bones of Cxcl12–GFP mice.
 - Cxcl12+Alpl− Adipo-CAR cells in the central BM predominantly ensheathed sinusoids, similar to LCM-SEq
 - Cxcl12+Alpl+ Osteo-CAR cells in the central BM typi-cally  showed  a  non-sinusoidal  localization  and  a  highly  reticular  morphology.
 - Cxcl12+Alpl+Sca1low Osteo-CAR cells were also frequently observed in  the  immediate  vicinity  of  Sca1+GFPdim  arterioles,  with  GFPhigh protrusions of Osteo-CAR intensively covering the arteriolar blood vessels
-  **non-sinusoidal  localization of Osteo-CAR cells** and qualitatively confirm that these cells  localize  to  **both  arterioles  and  non-vascular  regions**.


![](https://i.imgur.com/tfcolE4.jpg)
***Figure 5 a,** Single-cell gene expression levels of SM22 (Tagln) and Pdpn with the relevant cluster identity colour-coded (left). Immunofluorescence staining of SM22, Pdpn and CD31 in a BM arteriole (centre). Immunofluorescent staining of Pdpn, Sca1 and Pdgfra in a BM arteriole (right). Scale bars, 20 μm. **b**, Single-cell gene expression levels of Col1a1 and Pdpnwith the relevant cluster identity colour-coded (left). Immunofluorescence staining of Col1a1, Pdpn and DAPI at the endosteal surface (right). Scale bar, 20 μm. **c**, Immunofluorescence staining of Pdpn at the endosteum, cortical bone and periosteum. Scale bar, 50 μm.*

Then they use markers for different known populations of cells:
- smooth  muscle  cells  (SM22+Pdpn−)
- fibroblast  populations  (Pdpn+Pdfgr+)
- osteo-blasts (Pdpn−Col1a1+) and 
- arterial ECs (Pdpn−CD31+Sca1+)

![](https://i.imgur.com/ocgGou0.jpg)
***Figure 5. a,** Single-cell gene expression levels of SM22 (Tagln) and Pdpn with the relevant cluster identity colour-coded (left). Immunofluorescence staining of SM22, Pdpn and CD31 in a BM arteriole (centre). Immunofluorescent staining of Pdpn, Sca1 and Pdgfra in a BM arteriole (right). Scale bars, 20 µm. **b,** Single-cell gene expression levels of Col1a1 and Pdpn with the relevant cluster identity colour-coded (left). Immunofluorescence staining of Col1a1, Pdpn and DAPI at the endosteal surface (right). Scale bar, 20 µm. **c,** Immunofluorescence staining of Pdpn at the endosteum, cortical bone and periosteum. Scale bar, 50 µm. All imaging experiments were repeated independently two times with similar results.*

This way they further characterize the unknown subgroups:
- As with LCM-seq  data,  CD31/Sca1-expressing  arterioles were enveloped by SM22+Pdpn− smooth muscle cells and Pdpn+fibroblasts -> **Arteriolar  fibroblasts** seem  to  be  the  **cellular  source for the collagen layer** of the tunica externa **surrounding arterioles**.
- **Pdpn+Col1a-low fibroblasts  localizing  to  the  bone-facing  side  of  the  endosteal  lining** made up of Pdpn−Col1ahigh osteoblasts.
-  **Pdpn+cells**  were  also  found  at  the  **cortical**  bone  and  periosteal  regions,  which raises the possibility that fibroblast-like cells similar to those in the central marrow also derive from distal regions of the BM.

This validates the ability of the approach to identify previously unknown cell types, such as **Adipo- and Osteo-CAR cells**, and spatially allocate them in the BM.

### 2.4 The  spatial  relationships  of  the  BM-resident  cell  types  can  be  accurately  predicted  based  on  scRNAseq  data.  

 **spatial  relationships** of  cell  types  are  established  and  maintained  in  complex organs such as the BM??
 
 - the expression of cell adhesion molecules rep-resents an important mechanism that translates basic genetic infor-mation into complex three-dimensional patterns of cells in tissues.
 - List of well-annotated cellular adhesion receptors and their cognate plasma membrane or extracellular matrix  bound  ligands ->  RNA-Magnet  algorithm  to  investigate  whether  the  cell-type-specific  localizations  of  BM  populations can  be  predicted  by  the  differential  expression  of  cell  adhesion molecules.
     - RNA-Magnet  **predicts  the  poten-tial  physical  interactions**  between  single  cells  and  selected  attrac-tor  populations  (anchors)  based  on  the  **expression  patterns  of  the  cell-surface  receptors  and  their  cognate  surface-expressed  binding  partners**. 
         - RNA-Magnet adhesiveness:scores for the strength of attraction
         - RNa-Magnet location:  a direction indicating the attractor population the cell is most attracted to 

    -    **four  anchor  populations ** representing  the  following  niches:  osteoblasts  for the endosteal niche, sinusoidal ECs for the sinusoidal niche, as well as arterial ECs and smooth muscle cells to represent the arterio-lar niches.

![](https://i.imgur.com/vegV2NG.png)
***Figure 6. a,** t-SNE highlighting the cell type each cell is most likely to physically interact with (RNA-Magnet location, indicated by colour) and the estimated strength of adhesion (RNA-Magnet adhesiveness, indicated by opacity). n = 7,497 cells. **b,** Comparison of the estimated strength of adhesion (RNA-Magnet score) with the degree to which each cell type is differentially localized between niches (spatial transcriptomics; see also Fig. 3). n = 25 cell types are shown, all HSPC subtypes were summarized as a single population. The Pearson’s correlation (R) and its associated P value are shown. **c,** Heatmap depicting a summary of the inferred localization based on RNA-Magnet (left). The fraction of cells assigned to a certain niche is colour-coded according to the legend. Pearson’s correlation between the RNA-Magnet estimate of localization and the LCM-seq estimate of localization across n = 3 niches (right.

- **predicted adhesiveness** of BM populations to distinct niches c**orrelated strongly with their degree of differen-tial  localization**,  as  measured  by  spatial  transcriptomics.
- Localization  was  also  recapitulated for all populations, including the differential localization of Adipo- and Osteo-CAR cells to the sinu-soidal and arteriolar endothelia, respectively.
- smooth muscle cells were most attracted to arterial ECs, whereas arteriolar fibroblasts adhered to smooth muscles, **recapitulating the consecu-tive layering observed in blood vessels** with the tunica intima (endo-thelial cells) surrounded by the tunica media (smooth muscle cells) and the tunica externa (extracellular matrix produced by arteriolar fibroblasts).

### 2.3  Cellular  and  spatial  sources  of  cytokines  and  growth  factors  in  the BM
biological processes occurring in the BM are thought to  be  mediated  by  the  **coordinated  action**  of  a  diverse  set  of  cyto-kines  and  growth  factors. -> dentity  of  the  cytokine-producing  cells  and  their  organization  into  spatial  and  functional  BM  niches  remain  poorly  understood. 

- Cxcl12  and  Kitl  (Scf)  are  indeed  expressed  by  arterial  ECs36  and  some  mesenchymal  cell  types9.  However,  their  expression  was  several  orders  of  magnitude  higher in the Adipo- and Osteo-CAR populations 

To confirm Cxcl12 expression from Adipo- and Osteo-CAR cells at the protein level, we developed fluorescence-activated-cell-sorting  (FACS)  marker  strategies  to  discriminate  these  cells  types  and  confirmed  them  using  FACS-based  index-scRNAseq 

 Comparative  analyses  demonstrated  that:
 - Adipo-CAR  cells  are CD45−CD71−Ter119−CD41−CD51+VCAM1+CD200midCD61low,  whereas  
 - Osteo-CARs  and  NG2+  MSCs  expressed  CD200  and  CD61  at  high  levels  


![](https://i.imgur.com/9keUY98.jpg)
**Figure 7. a**, Contribution of different cell types to distinct cytokine pools. The mean gene expression across all cells constituting each cell type is compared. **b,** Single-cell gene expression levels of Cd200 and Itgb3 (CD61) from scRNAseq data in CD45−Lin−CD71−Vcam1+ cells. MAGIC44 was used to impute the drop-out values. **c,** Surface marker levels of CD200 and CD61 from index-scRNAseq data (n = 91) in CD45−Lin−CD71−Vcam1+ cells (see Methods). The colour indicates the most similar cell type from the main dataset, as identified using scmap46. **d,** Intracellular FACS analyses of Cxcl12 expression in total BM, lineage-negative BM, Lin−CD31+ ECs and CD61/CD200 subpopulations of CD45−Lin−CD71−CD51+Vcam1+ CAR cells. Data are depicted as the mean and s.e.m. with n = 3 per group. Statistics were performed using an unpaired two-tailed t-test. The experiment was performed two times independently with a similar outcome.**e, Quantification of the number of growth factors (GFs) and cytokines expressed by each cell type and the fraction of total mRNA devoted to producing GFs and cytokines.**f,** Relative expression of cytokines and GFs in Adipo-CAR cells and fibroblasts. **g,** Expression of Cxcl12 (left), Kitl (middle) and summed expression of all cytokines and GFs (right) in the indicated niches from spatial transcriptomics data. The P values for differential expression of Cxcl12 and Kitl (left and middle) relative to non-vascular niches were computed using the moderated t-test used in limma/voom47 and corrected for multiple testing using the Benjamini–Hochberg method. Right, a two-sided Wilcoxon rank-sum test was applied. Arteriolar niches, n = 28; sinusoidal niches, n = 14; and non-vascular niches, n = 11. The boxplot elements are defined in Methods (‘Data visualization’ section).**h,** Expression of cytokines, chemokines and GFs in the different niches measured by LCM-seq. Only factors with significant differences between niches are included. *P < 0.001; **P < 0.01; *P < 0.05; and n.s., not significant.*

- Intracellular  flow cytometric analyses confirmed that both **CAR populations are the  main  producers  of  Cxcl12**,  whereas  endothelial  cells  produced  detectable but significantly lower levels.
-   C**AR  cell  populations  were  also  among  the  main  producers  of  key  cytokines  required  for  B-cell  and  myeloid  lineage  commitment**, such as Il7  and  m-CSF (Csf1;
-   **CAR cell populations produced the highest numbers of  distinct  cytokines  and  growth  factors**,  and  attributed  the  highest  proportion  of  transcriptional  activity  to  cytokine  production,  suggesting  that  they  act  as  ‘professional  cytokine-producing  cells’ 

Differential localization of professional cytokine-producing cells to  cellular  scaffolds  results  in  the  establishment  of  specific  micro-niches.  In  line  with  this,  s**patial  transcriptomics  revealed  that  the  five niches investigated displayed unique production patterns of sig-nalling  mediators** 

These  observations  suggest  that  the  specific  localization  of **professional cytokine-producing BM-resident cells** results in the establishment of unique niches with both the **arteriolar and sinusoidal niches** as key sites for the production of HSC maintenance and differentiation factors.

### 2.5 Systems-level  analysis  of  intercellular  signalling  interactions  of  BM cell types

applied R**NA-Magnet to soluble signalling mediators**  (for  example  cytokines,  growth  factors  and  so  on)  and  their  **receptors** to gain a systems-level overview of potential intercellular signalling interactions.

- RNA-Magnet incorporates information on surface receptors with low messenger RNA expression, is based on a highly curated list of ligand–receptor pairs -> supervised

![](https://i.imgur.com/IUTjNdd.jpg)
***Figure 8. a**, Inference of signalling interactions between cell types by RNA-Magnet. If a cell type is enriched in the expression of ligands for receptors expressed by a second cell type, a line is drawn between these cell types, with the colour indicating the ligand-producing cell type and the line width indicating the strength of the enrichment. See Methods for details and Extended Data Fig. 9e for a fully labelled version of the figure.**b,** Summary of the niche composition, as estimated from LCM-seq (see also Fig. 3f). **c,** Inference of the signalling interactions between niches and cell types. The line width indicates the strength of the enrichment for expression of the ligand–receptors pairs. The cell types are arranged as in b. Meg., megakaryocyte; ery., erythroid; eo., eosinophil; baso., basophil; prog., progenitor.

- **RNA-Magnet  analyses  formed  two  disconnected  signalling  clusters**  consisting  of  either  **mature  immune  or  non-haematopoietic  cells**,  suggesting  that  immune  and  non-immune  cells  preferentially  communicate  within  their  respective  group.
- HSPC  populations  frequently  received  signals  from  non-haematopoietic  cells,  suggesting  that  HSPCs  gradually  switch  from  a  mesenchy-mal to an immune signalling niche following lineage commitment.
- **CAR  cell  populations  were  the  most  important  source of the signal**s sensed by all myeloid and lymphoid progenitors.

## 3.  Discussion

- single-cell  and  spatially  resolved  tran-scriptomics  to  transcriptionally  map  all  major  BM-resident  cell  types  and  spatially  allocate  them  to  distinct  BM  niches.
- cellular  and  spatial  origins  of  key  cytokines  regulating  BM  haematopoiesis.  
- Key  HSC  factors,  such  as  Cxcl12  and  Scf,  are produced by  two  distinct  and  previously  unappreciated  subpopulations,  which  we  have  termed  Osteo-  and  Adipo-CAR  cells  according  to  their  gene  expression  profile.
- CAR  cell  subsets  produce  the  high-est  quantities  of  cytokines  among  all  BM  cell  types,  including  the  main  cytokines  mediating  myeloid  and  lymphoid  differentiation,  in  line  with  a  recent  study  demonstrating  that  IL7  and  Cxcl12  are  produced by the same BM cell type.
- CAR cells act  as  ‘professional  cytokine-producing  cells’  and  constitute  cen-tral  niche  cells  orchestrating  different  aspects  of  haematopoiesis. 
- dipo-CAR cells localize to sinusoidal endothelia, Osteo-CAR cells localize to the non-vascular regions or cover arteriolar endothelia. 
- arteriolar  and  sinusoidal  vascular  scaffolds  represent  key  sites  for  the production of factors required for HSC maintenance and differ-entiation, with the Adipo- and Osteo-CAR cell subsets constituting central cellular hubs

# RNA Magent
https://static-content.springer.com/esm/art%3A10.1038%2Fs41556-019-0439-6/MediaObjects/41556_2019_439_MOESM1_ESM.pdf

- Surface receptors are frequently expressed lowly at the mRNA level
- Low efficiencies of reverse transcription (‘dropout’) therefore cause these genes to frequently be missed at the single cell level
- While their expression can be imputed using MAGIC, the absolute mRNA level of receptor genes is a poor proxy of absolute amounts of protein

Transformed MAGIC estimates of gene expression to a fuzzy logic variable to encode if the gene is ‘expressed’ or ‘not expressed’:

![](https://i.imgur.com/K3eThm5.png)
where xi,g is the MAGIC gene expression estimate of a gene g in cell i. Parameters were set to x0 = 0.5 and k = 10. Here, x0 specifies the threshold value as a fraction of maximal gene expression above which the gene is considered expressed, and k specifies the degree of fuzziness, with f(x) tending towards Boolean logic for k → ∞. 

- Scoring of interaction strength between single cells and reference cell populations

RNA-Magnet provides scores of interaction strengths between a ‘sending’ cell population (i.e. a ligand-secreting cell population) and target ‘receiver’ cells. We therefore first computed population means of gene expression, and then applied a fuzzy logic AND operation to  6 determine if the ligand l is expressed by the sending population K, and the receptor r is expressed by the recipient cell c:

![](https://i.imgur.com/kcCtSm7.png)

Where R is the set of all receptors and L(r) is the set of all ligands binding to receptor r. In the case of physical (receptor-receptor or receptor-ECM) interactions, we assume that if r is a receptor of l, l is also a receptor of r. S(c, K) was then normalised to sum to 1 across all populations included.

- highly similar cell types display differential localization -> considering the scores relative to an average score seen in similar cells: We computed a local background level of interaction scores for each cell c and sending population K by
