Identifying critical state of complex diseases by single-sample Kullback–Leibler divergence

Molecular Therapy - Oncolytics

et al. 2021

Self Cite

Increasing evidence indicates that mature B cells in the adjacent tumor tissue, both as an intermediate state, are vital in advanced colorectal cancer (CRC), which is associated with a low survival rate. Developing predictive biomarkers that detect the tipping point of mature B cells before lymph node metastasis in CRC is critical to prevent irreversible deterioration. We analyzed B cells in the adjacent tissues of CRC samples from different stages using the dynamic network biomarker (DNB) method. Single-cell profiling of 725 CRC-derived B cells revealed the emergence of a mature B cell subtype. Using the DNB method, we identified stage II as a critical period before lymph node metastasis and that reversed difference genes triggered by DNBs were enriched in the Janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway involving B cell immune capability. DHX9 (DEAH-box helicase 9) was a specific para-cancerous tissue DNB key gene. The dynamic expression levels of DHX9 and its proximate network genes involved in B cell-related pathways were reversed at the network level from stage I to III. In summary, DHX9 in mature B cells of CRC-adjacent tissues may serve as a predictable biomarker and a potential immune target in CRC progression.

Section: Discussionmentioning

confidence: 99%

Single-cell transcriptomics reveal DHX9 in mature B cell as a dynamic network biomarker before lymph node metastasis in CRC

Liu

Molecular Therapy - Oncolytics

et al. 2021

Self Cite

“…Generally, the dynamical process of early embryonic development can be regarded as the evolution of a nonlinear dynamical system, while the cell fate transition is viewed as a drastic or qualitative state shift at a bifurcation point [8] . Similar to disease progression [11] , [20] , this dynamical process is modeled as three states or stages ( Figure 1 C): 1) a before-transition stage with high resilience; 2) a critical stage, which is the tipping point or cell fate transition with low resilience; and 3) an after-transition stage, which is another stable state with high resilience.…”

Section: Methodsmentioning

confidence: 99%

scGET: Predicting Cell Fate Transition During Early Embryonic Development by Single-Cell Graph Entropy

Genomics, Proteomics &Amp; Bioinformatics

Han

Zhang

et al. 2021

Self Cite

During early embryonic development, cell fate commitment represents a critical transition or “tipping point” of embryonic differentiation, at which there is a drastic and qualitative shift of the cell populations. In this study, we presented a computational approach, scGET, to explore the gene–gene associations based on single-cell RNA sequencing (scRNA-seq) data for critical transition prediction. Specifically, by transforming the gene expression data to the local network entropy, the single-cell graph entropy (SGE) value quantitatively characterizes the stability and criticality of gene regulatory networks among cell populations and thus can be employed to detect the critical signal of cell fate or lineage commitment at the single-cell level. Being applied to five scRNA-seq datasets of embryonic differentiation, scGET accurately predicts all the impending cell fate transitions. After identifying the “dark genes” that are non-differentially expressed genes but sensitive to the SGE value, the underlying signaling mechanisms were revealed, suggesting that the synergy of dark genes and their downstream targets may play a key role in various cell development processes. The application in all five datasets demonstrates the effectiveness of scGET in analyzing scRNA-seq data from a network perspective and its potential to track the dynamics of cell differentiation. The source code of scGET is accessible at https://github.com/zhongjiayuna/scGET_Project.

“…Generally, the dynamical process of early embryonic development can be regarded as the evolution of a nonlinear dynamical system, while the cell fate transition is viewed as a drastic or qualitative state shift at a bifurcation point [10]. Similar to disease progression [13,18], this dynamical process is modeled as three states or stages (Figure 1C): (1) a before-transition stage with high resilience; (2) a critical stage, which is the tipping point or cell fate transition with low resilience; (3) an after-transition stage, which is another stable state with high resilience.…”

Section: Theoretical Basismentioning

confidence: 99%

Predicting cell fate commitment of embryonic differentiation by single-cell graph entropy

Han

Zhang

et al. 2020

Preprint

Self Cite

16Cell fate commitment occurs during early embryonic development, that is, the embryonic 17 differentiation sometimes undergoes a critical phase transition or "tipping point" of cell fate 18 commitment, at which there is a drastic or qualitative shift of the cell populations. In this study, we 19 presented a novel computational approach, the single-cell graph entropy (SGE), to explore the gene-20 gene associations among cell populations based on single-cell RNA sequencing (scRNA-seq) data. 21 Specifically, by transforming the sparse and fluctuating gene expression data to the stable local network 22 entropy, the SGE score quantitatively characterizes the criticality of gene regulatory networks among 23 cell populations, and thus can be employed to predict the tipping point of cell fate or lineage 24 commitment at the single cell level. The proposed SGE method was applied to five scRNA-seq datasets. 25 For all these datasets of embryonic differentiation, SGE effectively captures the signal of the 26 impending cell fate transitions, which cannot be detected by gene expressions. Some "dark" genes that 27 are non-differential but sensitive to SGE values were revealed. The successful identification of critical 28 transition for all five datasets demonstrates the effectiveness of our method in analyzing scRNA-seq 29 data from a network perspective, and the potential of SGE to track the dynamics of cell differentiation. 30 31 gene; cell fate commitment. 33 34 35Predicting cell-fate commitment by SGE 2 This is a provisional file, not the final typeset article 36 1. Introduction 37 Complex systems may switch abruptly to a contrasting state through a critical transition [1]. In recent 38 years, detecting critical transitions for general systems, such as ecosystems systems [2-3], climates 39 systems [4-5], financial systems [6,7], and epidemic model [8][9], has drawn more and more attentions. 40In biomedical fields, the rapid growth of single-cell datasets has shed new light on the complex 41 mechanisms of cellular heterogeneity. In these single-cell experiments, the cell fate commitment 42 represents a critical state transition or "tipping point" at which complex systems undergo a qualitative 43 shift. Characterizing and predicting such critical transition is crucial for patient-specific disease 44 modeling and drug testing [10]. Recent studies provided a plethora of statistical quantities such as 45 variance, correlation coefficient, and coordination of gene expression, to detect a cell fate transition of 46 embryonic differentiation [10,11]. However, these statistical quantities mainly focused on the analyses 47 at the gene expression level, while single-cell RNA sequencing (scRNA-seq) may offer more 48 information of an insight into the cell-specific network systems. In contrast to gene expression, cell-49 specific network is a stable form against the time and condition [12], and thus reliably characterize the 50 biological processes such as cell fate commitment. Such a network system is viewed as a nonlinear 5...