iGEM 2022 — Team pumc-china

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#### April

- We decided to paticipate in igem2022, as the first team ever paticipating igem
- Doctor Yang Chen, Qiaoyi Shen and I (Zhizhuo Chen) registered for the team.

#### May

- We decide to choose EMT as our project analysis target.
- Doctor Yang Chen provides a scRNA-seq dataset including 22 HCC samples

#### June

- We tried deep reinforcement learning to study EMT, but failed

#### July

- We successfully reconstructed cell lineages with traditional graph-based methods
- We found lineage clustering as a good tool for dissecting different lineages.
- We selected our track.

#### August

- We furtehr validated our method with another dataset
- We choose GAM to autonomously find expression patterns.

#### Semptember

- We prepared for Promotion Video
- We organized our codes

#### October

- We edited the wiki
- We uploaded the software
- We prepared for presentation video

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## First iteration

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**Problem:** We first tried deep reinforcement learning to find potent paths. This takes each cell as an agent and let it learn paths toward EMT autonomously. However, we later gave up this method as reinforcement learning methods are quite hard to converge. Meanwhile, the results from deep learning networks are hard to explain, which also motivated us to look for another method.

**Solution**: We finally chose traditional graph-based path finding method, which is easier to perform and explain.

## Second iteration

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**Problem:** We originally performed the algorithm in PCA space, trying to find a path based on Euclidean distance. This method eventually failed we could not find a steady path, which is shown in the figure below.

![](https://static.igem.wiki/teams/4318/wiki/wiki/e-1.jpg)

**Solution**: We chose to made use of a Gaussian kernel to measure the distance between sample points in the dataset.

## summary

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Each engineering algorithm needs several iterations to reach best performance. We select two of them for presentation here, and we will continue to refine our metric in the future.

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#### Code and Data avaibility

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All of the source code to reproduce hematopoietic cell differentiation results is available at gitlab:[gitlab.igem.org/2022/software-tools/pumc-china](https://gitlab.igem.org/2022/software-tools/pumc-china)

The HCC dataset used in our project is from another unpublished work, so we won't make it public until the work is published.

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## Single-cell analysis reveals a mesenchymal type cell subpopulation.

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![Figure1](https://static.igem.wiki/teams/4318/wiki/wiki/figure1-1.png)
![Figure1](https://static.igem.wiki/teams/4318/wiki/wiki/figure1-2.png)

**Figure 1. scRNA-seq reveals a mesenchymal type cell subpopulation**

Left: The single cell RNA sequencing reveals 43 distinct cell subpopulations. Right: The higher expression of mesenchymal markers SNAI1, MMP7 and SNAI2 in cluster 35.

We collect tumor samples from 22 hepatocellular carcinoma (HCC) patients and perform scRNA-seq on these samples. After data integration and denosing, we find 43 distinct cell populations1-3. Among these cell populations, cluster 35 highly expresses mesenchymal cell markers SNAI1, MMP7, SNAI2, etc. These genes are believed to be involved in triggerring cell epithelial to mensenchymal transition (EMT) program, helping tumor cell destroy extracelluar matrix and eventually contributing to tumor metastasis and invasion4.

## Trajectory inference and lineage clustering find potent paths toward mesenchymal state.

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![Figure2](https://static.igem.wiki/teams/4318/wiki/wiki/figure2-1.png)
![Figure2](https://static.igem.wiki/teams/4318/wiki/wiki/figure2-2.png)

**Figure 2. Graph based methods reveals potent paths toward mesenchymal state.**

Left: Graph based methods find potent paths toward EMT, different path clusters are marked with different colors. Right: Measure path simmilarity with Hausdorff distance and project the reuslts with mds reduction.

Believing that cluster 35 is in mesenchymal state and may contribute to tumor metastasis, we next qusetion that what gene expression patterns will happen during EMT5-6. Though there are many availble algorithrms currently, we decide to develop a new unsupervised method to solve this problem. Our method involves the following steps:
1. Project cells into low dimensions with decomposition methods such as PCA, Diffusion Map or UMAP.
2. Pre-cluster cells into small groups with a revised k-means clustering method.
3. Measure the connectivity between small groups with Gaussian kernel.
4. Use graph-based pathfinding algorithrm to find paths toward the target cell group.
5. Measure path simmilarity with Hausdorff distance and cluster paths with k-means
As a result, we successfully find distint paths toward cluster 35, as you can see in the Figure 2.

## Validation on hemopoietic cell differentiation dataset reconstructs three distint differentiation types.

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![Figure3](https://static.igem.wiki/teams/4318/wiki/wiki/figure3-1.png)
![Figure3](https://static.igem.wiki/teams/4318/wiki/wiki/figure3-2.png)
![Figure3](https://static.igem.wiki/teams/4318/wiki/wiki/figure3-3.png)
![Figure3](https://static.igem.wiki/teams/4318/wiki/wiki/figure3-4.png)
![Figure3](https://static.igem.wiki/teams/4318/wiki/wiki/figure3-5.png)

**Figure 3. Heatmap shows potent fates of hematopoietic stem cell differentiation**

The 5 potent differentiation fates of hematopoietic stem cell is plotted with heatmaps. The left most of each heatmap indicates the original stem state, which then differentiate into erthrocytes (2, 3, 4), monocytes (5), neutrophils (1) respectively.

In order to validate our method, we decide to test the same pipeline in a hematopoietic cell differentiation dataset includes 2730 hematopoietic cells7. According to the expression pattern of known markers, we find that our method successfully reconstructs erthrocyte, monocyte and neutrophil differentiation pathways in an unsupervised manner (Figure 3). This motivates us to further analyze the expression patterns found during EMT of HCC samples.

## GAM fitness reveals different gene expression patterns toward EMT of HCC cells.

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![Figure4](https://static.igem.wiki/teams/4318/wiki/wiki/figure4-1.png)
![Figure4](https://static.igem.wiki/teams/4318/wiki/wiki/figure4-2.jpg)

**Figure 4. GAM to autonomously find different expression patterns of scaled gene counts**

Left: The GAM fitted gene expression patterns are projected into 2-dimension with mds reduction. Right: GAM fitted scaled counts across pseudotime.

In order to filter out possible gene expression pattern that are revalent with EMT program in different lineages, we use generalized addictive model (GAM) to fit the scaled gene counts acrocess the path8. Similar method has been discribed by Koen Van den Berge et al9. Here, we manually set a R2 of 0.6 as a threshold of good fit model, which will then be considered in later analysis. Using this method, we find three gene expression patterns toward EMT, including down regulation, up regulation ,down first and up next regulation (Figure 4. Right).

## Lieange 6 is revalent to better clinical outcome.

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![Figure5](https://static.igem.wiki/teams/4318/wiki/wiki/figure5-1.png)
![Figure5](https://static.igem.wiki/teams/4318/wiki/wiki/figure5-2.jpg)

**Figure 5.Lieange 6 is revalent to better clinical outcome and specific inflammatory reaction expression pattern.**

Left: Kaplan-Meier analysis reveals that lineage 6 (orange line) is associated with better clinical outcomes. Right: Lineage 6 specific down regulation pattern involved a set of inflammatory reaction related genes.

We next ask that if there is any lineage associated with clinical outcomes and what is its specific expression pattern. In order to do this, We collect bulk RNA-seq data with clinical outcome information from HCC database and impute their fraction of different lineages with previous described method10-11. Kaplan-Meier analysis finds that higher fraction of lineage 6 in patients is associated with better clinical outcomes (Figure 5. Left). A t-test analysis filters lineage 6 specific expression patterns and gene ontology analysis is performed to enrich these expression patterns12 (Figure 5. Right). As a result, we find this lineage has a enrichment of downregulated inflammation genes during EMT, which may inhibit tumor cells from entering mesenchymal state and eventually causing metastasis.

#### Future

In summary, our method provides a new insight into the cell differentiation dynamics. The major darwback of our project is that the result of method needs to be further analyzed and validated at clinics and cell lines. We hope our method could help doctors and researchers to better analyze the dynamics of complex pathological samples, which will eventually be used to help more patients suffering from tumor or other diseases.

#### Reference

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- 2. Wolf FA, Angerer P, Theis FJ. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 2018 Feb 6;19(1):15. doi: 10.1186/s13059-017-1382-0. PMID: 29409532; PMCID: PMC5802054.
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