# Method for single-cell level cell-type annotation using marker genes, in scRNA-Seq data

Audrey Onfroy, Fanny Coulpier, Katarzyna J. Radomska, Denis Thieffry, Piotr Topilko

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Audrey Onfroy, Fanny Coulpier, Katarzyna J. Radomska, Denis Thieffry, Piotr Topilko. Method for single-cell level cell-type annotation using marker genes, in scRNA-Seq data. JOBIM 2022, Jul 2022, Rennes, France. 2022. [hal-05055376](https://hal.science/hal-05055376v1)

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https://hal.science/hal-05055376v1

Submitted on 4 May 2025

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# Deciphering mechanisms driving malignant transformation in Neurofibromatosis Type I from single-cell transcriptomics

Audrey ONFROY¹,², Fanny COULPIER¹, Katarzyna RADOMSKA¹, Denis THIEFFRY² and Piotr TOPILKO¹

1 Institut Mondor de Recherche Biomédicale, Inserm U955 - Team 9, Créteil, France
2 Institut de Biologie de l'École Normale Supérieure, UMR CNRS 8197, Inserm U1024, PSL Université, Paris, France

Contact : audrey.onfroy@inserm.fr

## NF1 IS A GENETIC DISEASE

<figure><img src="image_2.png" /></figure>

* Affects 1 / 3,000 births worldwide

* Bi-allelic inactivation of **NF1** gene in Schwann cells

* A lot of symptoms, including development of **neurofibromas** (NF) : benign nerve sheaths tumors

<figure><img src="image_3.png" /></figure>

no treatment to date

*MPNST = malignant peripheral nerve sheaths tumor

## Nf1-KO MOUSE MODEL

<figure><img src="image_4.png" /></figure>

* Bi-allelic inactivation of **Nf1** gene in Schwann cells

* **Nf1** mutant cells are traced by the **Tomato** fluorescent reporter

* **Nf1-KO** mice share common features with NF1 patients, including development of pNFs that **spontaneously** evolve into MPNSTs through a dysplastic stage

What are the molecular events driving malignant transformation of pNF into MPNST, in tumor forming cell populations, in the mouse model and in NF1 patients ?

## SINGLE-CELL TRANSCRIPTOMICS DATA

**Single-cell RNA sequencing** : for each biological sample, we have a genes x cells expression matrix

<figure><img src="image_5.png" /></figure>

How to annotate cells for cell type ?

* cluster cells into groups with similar expression profiles

* manual or automatic annotation, using existing references

## MARKERS IDENTIFICATION IN TUMOR FORMING POPULATIONS

### Motivations

* **No existing reference** markers for tumor cells in NF1 disease to date

* In *Nf1*-KO mice, *Nf1* mutant cells are traced by the **Tomato** fluorescent reporter

* All *Nf1*-KO mice datasets share similar **tumor forming cell populations**, for which we know few markers

* Distinction between tumor cells and fibroblasts in NF1 patients is difficult

→ Refine population markers in the specific context of NF1

### Methods

#### 1- Input

* cell types and known markers :

<table><thead><tr><td>cell type</td><td>marker.s</td></tr></thead><tbody><tr><td>A</td><td>α</td></tr><tr><td>B</td><td>β, δ</td></tr><tr><td>C</td><td>γ</td></tr></tbody></table>

* scRNA-Seq dataset containing cell populations of interest

<figure><img src="image_6.png" /></figure>

2- Identification of positive and negative populations
for each cell type

<figure><img src="image_7.png" /></figure>

Marker.s expression score (left) helps to define cells (right) related to the corresponding cell type.

3- Identification of positive markers candidates
by differential expression between ω⁺ and ω⁻

4- Markers selection, for each cell type

Goal : maximize **representativeness** (r) and **selectivity** (s)

Criteria : $max(r^2 + s^2)$

Keep top 50 markers

**representativeness** (r) : % of cells in ω⁺ having a positive number of counts for the marker

**selectivity** (s) :

% of cells in ω⁻ having no count for the marker

<figure><img src="image_8.png" /></figure>

5- Output : markers for each cell type

The positive **markers** for cell type A (pm(A)) are $a_1, ..., a_n$. The negative markers are defined as the union of positive markers for all other cell types, i.e. $pm(A) = pm(not(A))$.

The intersection between pm(A) and pm(not(A)) is not necessarily empty : a gene can be a marker for several cell types.

<figure><img src="image_9.png" /></figure>

## AUTOMATIC ANNOTATION AT A SINGLE CELL LEVEL

### Motivations

* Cluster-based annotation can lead to confusion between cell types

* **Expression scores** are used to predict cell cycle phase in Seurat's package [3]

* Using **negative markers** can strengthen cell type annotation

→ Score positive and negative markers expression levels, to annotate **single cells**

### Methods

#### 1- Input

* cell types and positive markers :

<table><thead><tr><td>cell type</td><td>markers</td></tr></thead><tbody><tr><td>A</td><td>a<sub>1</sub>, ..., a<sub>n</sub></td></tr><tr><td>B</td><td>b<sub>1</sub>, ..., b<sub>m</sub></td></tr><tr><td>C</td><td>c<sub>1</sub>, ..., c<sub>p</sub></td></tr><tr><td>...</td><td>...</td></tr><tr><td>O</td><td>o<sub>1</sub>, ..., o<sub>m</sub></td></tr></tbody></table>

* scRNA-Seq dataset to annotate

<figure><img src="image_10.png" /></figure>

2- Markers expression scoring with Seurat

<figure><img src="image_11.png" /></figure>

For cell type i and cell j :

* $P_{i,j} > 0$ : j expresses positive markers for i

* $N_{i,j} > 0$ : j expresses negative markers for i

* $P_{i,j} - |N_{i,j}| > 0$ : j may be from cell type i

3- Difference between both score matrices

<figure><img src="image_12.png" /></figure>

$$D = P - |N|$$

For each single cell (column), **cell type** can be annotated from the row names corresponding to the highest score.

## RESULTS

* We used **4 Nf1-KO** mice datasets to generate markers for each cell type

* We applied the annotation on **16 Nf1-KO** mice datasets

Immune cell type annotation is consistent with the results given by the Tumor Immune Cell Atlas [4]

Annotation our of data with this atlas :

<figure><img src="image_13.png" /></figure>

DC : dendritic cells

<figure><img src="image_14.png" /></figure>

New identified markers strengthen the annotation

known markers : 1-3 markers / population

new markers : 50 markers / population

## TAKE-HOME MESSAGES

### Context

* NF1 scRNA-Seq datasets share similar cell populations

* In *Nf1*-KO mouse model, tumor cells express the **Tomato** reporter gene

* To date, there is no marker to identify tumor cells in NF1 scRNA-Seq datasets

**Cell type annotation at a single cell level**

* Identification of new markers leads to more **robust** cellular signatures

* Expression scoring enables to keep track of **overlapping signatures**

* **No clustering** is required : single cell annotation enables the identification of rare populations and the distinction of closely related populations

Cell type annotation at a single cell level, for 50,036 cells

Annotation pipeline runtime : 435 seconds (7.3 minutes)

<figure><img src="image_15.png" /></figure>

## PROSPECTS

**Add annotation quality metrics**

* **ambiguity metric** : relative weight of the retained cell type compared all possible types

* **competition metric** : number of cell types with "high" score

**Perform a sensitivity analysis of the annotation, regarding :**

* the **number of positive markers** identified for each cell type : impact on runtime ?

* the number, or **proportion, of cells** taken into account to identify positive markers for one cell type

**Apply this approach to other NF1 scRNA-Seq datasets**, and build a NF1 atlas

**Analyse novel types of data** : scRNA-Seq long read (**isoforms**) and single-nuclei ATAC-Seq (**chromatin accessibility**)

Cell type annotation is robust to Tomato expression consideration

Tomato expression level enables tumor cells identification

<figure><img src="image_16.png" /></figure>

Without considering **Tomato** as a marker, cell type annotation changes for only 1 % of cells.

<figure><img src="image_17.png" /></figure>

Most of those 498 cells whose annotation changes are tumor cells (A), which becomes mis-annotated as stromal cells (H, M, B).

Expression scores enable to keep track of overlapping signatures

During malignant transformation in NF1, tumor cells (A) progress towards a mesenchymal profile, related to fibroblasts (B), which are tissue support cells.

<figure><img src="image_18.png" /></figure>

Cells with annotation ambiguity between fibroblasts and tumor cells types

Cells with high score for tumor cell type (A)

<figure><img src="image_19.png" /></figure>

## REFERENCES

[1] Radomska et al., Cellular Origin, Tumor Progression, and Pathogenic Mechanisms of Cutaneous Neurofibromas Revealed by Mice with Nf1 Knockout in Boundary Cap Cells, *Cancer Discovery* (2019)

[2] Sun et al., Stem-like cells drive NF1-associated MPNST functional heterogeneity and tumor progression, *Cell Stem Cell* (2021)

[3] Stuart et al., Comprehensive Integration of Single-Cell Data, *Cell* (2019)

[4] Nieto et al., A single-cell tumor immune atlas for precision oncology, *Genome Research* (2021)