# scInTime: A Computational Method Leveraging Single-Cell Trajectory and Gene Regulatory Networks to Identify Master Regulators of Cellular Differentiation

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

#### 2.1. Data Sets

#### 2.2. Pseudotime Determination for Cells

#### 2.3. Time-Series scGRN Construction and Generate the Regression Coefficient Matrix

#### 2.4. Code Availability

## 3. Results

#### 3.1. The scInTime Architecture

#### 3.1.1. Construct Pseudotime-Series Gene Regulatory Networks

#### 3.1.2. Regression Analysis

#### 3.1.3. Build Regression Coefficients Matrix

#### 3.1.4. Analysis of Regression Coefficients Matrix

#### 3.2. Applications to Time-Resolved scRNA-seq Data

#### 3.2.1. Application 1: Zebrafish Hindbrain

#### 3.2.2. Application 2: HNSCC Cell Line

#### 3.2.3. Application 3: Mouse Cardiomyocytes

## 4. Discussion

## Supplementary Materials

**A**) tSNE embedding of the regression coefficient matrix (2000 HVGs are highlighted in red). (

**B**) PHATE embedding of the regression coefficient matrix (the 2000 HVGs are highlighted in red). (

**C**) Visualization of four selected clusters on the PHATE embedding. Genes of each cluster are highlighted in red. Table S1: Results of GSEA analysis of the ranked gene list from scInTime analysis of the zebrafish hindbrain data. Table S2: Results of GSEA analysis of the ranked gene list from scInTime analysis of the HNSCC data. Table S3: Results of GSEA analysis of the ranked gene list from scInTime analysis of the mouse CM data.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 1.**The analytical framework of scInTime. (

**A**) Step 1: Order cells and construct networks. The cells are arranged and divided into 10 subgroups according to the pseudotime inferred using a pseudotime analysis tool, such as Monocle 3. Each group stands for a different transition state. Next, an scGRN is constructed for each group of cells. Each scGRN depicts gene-gene regulatory relationships at the given pseudotime point. A total of 10 scGRNs are constructed, each comprising the progressive transcriptomic profile of the given cellular process. (

**B**) Step 2: Fit a linear regression model to the 10 scGRNs. We fit a linear regression model for the 10 coefficients on the state index for each pair of genes. If this pair of genes’ regulation strength increases along the 10 stages, the regression coefficient will be positive and vice versa. (

**C**) Step 3: Generate the regression coefficient matrix. All the regression coefficients are collected into a single regression coefficients matrix, representing the global profile of all pairs of gene-gene regulation changes across the given cellular process. (

**D**) Dimensionality reduction and visualization of the regression coefficients matrix. We used tSNE embedding to reduce dimensionality on the regression coefficients matrix and visualize it. Each dot in the tSNE plot represents a gene embedded in the 2D plot based on its global profile in the regression coefficients matrix. The k-means clustering algorithm divides the genes in the regression coefficients matrix into 50 clusters. Enrichr analysis is performed on selected gene clusters. The genes are rank ordered according to their Mahalanobis distance to the embedding center. Then, the GSEA analysis is performed on the ranked gene list.

**Figure 2.**scInTime analysis with time-resolved scRNA-seq data from zebrafish hindbrain reveals gene expression programs in neurogenesis. (

**A**) Visualization of cells using UMAP embeddings plot. The cells are collected at 16 hpf, 24 hpf, and 44 hpf and color-coded accordingly. (

**B**) UMAP visualization of cells colored according to their pseudotime estimation (left panel) and differentiation potency (right panel). The pseudotime is estimated using Monocle 3. The level of differentiation potency is estimated using the CCAT algorithm. (

**C**) Expression level (log-transformed UMIs) of three representative genes, expressed highly at three different stages of development. (

**D**) Boxplot of cells in different developmental stages and their pseudotime estimates. (

**E**) Boxplot of cells in different developmental stages and their differentiation potency. (

**F**) Clustering of genes in the tSNE plot. The 5863 genes are clustered into 50 clusters using the k-means algorithm. Genes in the same cluster have a similar profile in the regression coefficient matrix. (

**G**) The position of four clusters #13, #14, #15, and #16 in the tSNE plot. Clusters #13 and #14 are in the peripheral region of the 2D tSNE plot; clusters #15 and #16 are in the central region. (

**H**) Results of enrichment analysis for the four clusters in (

**G**). N.S., not significant. (

**I**) GSEA enrichment plot for Pou5f1-dependent transcriptional network. (

**J**) tSNE visualization of genes (n = 5863) embedded in a 2D plot based on each gene’s profile in the regression coefficient matrix. In the plot, each dot represents a gene. The leading-edge genes for the Pou5f1-dependent transcriptional network given in GSEA analysis are highlighted red. (

**K**) A pseudotime-series heatmap showing the change of regulatory strength between genes as a function of time. The genes shown are the leading-edge genes in the Pou5f1-dependent transcriptional network, as in (

**I**,

**J**). (

**L**) Positions of 30 genes with the highest regulatory strength with dlb in the tSNE plot. The genes labeled with black stars are from the model showed in (

**O**), as in (

**N**). (

**M**) The expression level of three selected genes (dla, dlb, and her4.4) as a function of pseudotime shows a strong covarying relationship among them. None of these genes’ expressions show a monotonic relationship with pseudotime. (

**N**) Heatmap of regression coefficients between dlb and each of its top 30 regulated genes across 10 pseudotime intervals. The genes labeled with black stars are from the model showed in (

**O**), as in (

**L**,

**O**) Model of gene regulation: Products of genes dta and dtb activate genes in the delta-notch signaling pathway and then her4, which is involved in a negatively regulatory feedback loop suppressing dlb expression.

**Figure 3.**scInTime analysis with scRNA-seq data from HNSCC cell line reveals gene expression programs in cetuximab resistance development. (

**A**) Visualization of cells using tSNE embeddings plot. The cell line received cetuximab (treated) or PBS (untreated), and after five consecutive days, the cells were collected for scRNA-seq and color-coded accordingly. (

**B**) Two-dimensional representation of the data using tSNE. The trajectory inferred by Monocle 3 is displayed. (

**C**) Boxplot of cells in different treatment groups and their pseudotime estimates. (

**D**) Expression level (log-transformed UMIs) of four representative genes, expressed differentially at two different treatments. (

**E**) Clustering of genes in the tSNE plot. In the plot, each dot represents a gene. The 10,378 genes are clustered into 50 clusters using the k-means algorithm. Genes in the same cluster have a similar profile in the regression coefficient matrix. (

**F**) Positions of four clusters, #37, #1, #15, and #32, in the tSNE plot. Clusters #37 and #1 are in the peripheral region of the 2-D tSNE plot; clusters #15 and #32 are in the central region. (

**G**) Results of enrichment analysis for the four clusters in (

**G**). N.S., not significant. (

**H**) GSEA enrichment plot for Beta-3 integrin cell surface interactions. (

**I**) tSNE visualization of genes (n = 10,378) embedded in a 2D plot based on each gene’s profile in the regression coefficient matrix. The leading-edge genes for Beta-3 integrin cell surface interactions given in GSEA analysis are highlighted red. (

**J**) A pseudotime-series heatmap showing the change of regulatory strength between genes as a function of time. Genes shown are the leading-edge genes in Beta-3 integrin cell surface interactions, as in (

**I**). (

**K**) Position of 30 genes with highest regulatory strength with VIM in the tSNE plot. The genes labeled with black stars are from the model showed in (

**N**), as in (

**L**). (

**L**) Heatmap of regression coefficient values (levels of regulatory strength) between dlb and each of its top 30 regulated genes across 10 pseudotime intervals. The genes labeled with black stars are from the model showed in (

**N**), as in (

**K**). (

**M**) The expression level of three selected genes (CAV1, MT2A, and PFN1) as a function of pseudotime, showing a strong covarying relationship among them. None of these genes’ expressions show a monotonic relationship with pseudotime. (

**N**) The model of the regulations among the selected genes. The activation of the NF-κB signaling pathway will upregulate the transcription of VIM. VIM unidirectionally down-regulates CAV1. PFN1 and MT2A inhibit VIM expression by inhibiting the activation of the NF-κB signaling pathway.

**Figure 4.**scInTime analysis with time-resolved scRNA-seq data from mouse CMs reveals gene expression programs in CM maturation. (

**A**) Visualization of cells using tSNE embeddings plot. The cells are collected at 15 time points and color-coded accordingly. (

**B**) tSNE visualization of cells colored according to their pseudotime estimation. The pseudotime is estimated using Monocle 3. (

**C**) tSNE visualization of cells colored according to their differentiation potency. The differentiation potency is estimated using CCAT. (

**D**) Expression level (log-transformed UMIs) of four representative genes, expressed differentially at different time points. (

**E**) Boxplot of cells in different developmental stages (time points), colored by their pseudotime (up) and differentiation potency (bottom). (

**F**) Clustering of genes in the tSNE plot. In the plot, each dot represents a gene. The 6969 genes are clustered into 50 clusters using the k-means algorithm. Genes in the same cluster have a similar profile in the regression coefficient matrix. (

**G**) Positions of four clusters: #44, #40, #6, and #25, in the tSNE plot. Clusters #44 and #40 are located in the peripheral region of the 2D tSNE plot; clusters #6 and #25 are located in the central region. (

**H**) Results of enrichment analysis for the four clusters in (F). N.S., not significant; N.A., not applicable. (

**I**) GSEA enrichment plot for Pyruvate metabolism. (

**J**) tSNE visualization of genes (n = 6969) embedded in a 2D plot based on each gene’s profile in the regression coefficient matrix. The leading-edge genes for pyruvate metabolism given in GSEA analysis are highlighted in red. (

**K**) A pseudotime-series heatmap showing the change of regulatory strength between genes as a function of time. The genes shown are the leading-edge genes in Pyruvate metabolism, as in (

**I**,

**J**). (

**L**) Positions of the 30 genes with the highest regulatory strength with Ppara in the tSNE plot. The genes labeled with black stars are from the model showed in (

**O**), as in (

**M**). (

**M**) Heatmap of regression coefficient values (levels of regulatory strength) between Ppara and each of its top 30 regulated genes across 10 pseudotime intervals. The genes labeled with black stars are from the model showed in (

**O**), as in (

**L**). (

**N**) The expression level of three selected genes (Acaa2, Acadvl, and Ndufa10) as a function of pseudotime. (

**O**) The model of the regulations among the selected genes. Ppara, as an important regulator of fatty acid metabolism, activates fatty acid metabolism genes, including Acadvl and Acaa2, and genes related to mitochondrial energy metabolism (Ndufa). Ppara agonists can inhibit Cox genes expression.

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## Share and Cite

**MDPI and ACS Style**

Xu, Q.; Li, G.; Osorio, D.; Zhong, Y.; Yang, Y.; Lin, Y.-T.; Zhang, X.; Cai, J.J. scInTime: A Computational Method Leveraging Single-Cell Trajectory and Gene Regulatory Networks to Identify Master Regulators of Cellular Differentiation. *Genes* **2022**, *13*, 371.
https://doi.org/10.3390/genes13020371

**AMA Style**

Xu Q, Li G, Osorio D, Zhong Y, Yang Y, Lin Y-T, Zhang X, Cai JJ. scInTime: A Computational Method Leveraging Single-Cell Trajectory and Gene Regulatory Networks to Identify Master Regulators of Cellular Differentiation. *Genes*. 2022; 13(2):371.
https://doi.org/10.3390/genes13020371

**Chicago/Turabian Style**

Xu, Qian, Guanxun Li, Daniel Osorio, Yan Zhong, Yongjian Yang, Yu-Te Lin, Xiuren Zhang, and James J. Cai. 2022. "scInTime: A Computational Method Leveraging Single-Cell Trajectory and Gene Regulatory Networks to Identify Master Regulators of Cellular Differentiation" *Genes* 13, no. 2: 371.
https://doi.org/10.3390/genes13020371