Dynamic network biomarker factors orchestrate cell-fate determination at tipping points during hESC differentiation

Li, Lin; Yang, Xu; Yan, Lili; Li, Xiao; Li, Fēi; Liu, Zhuang; Zhang, Chuanchao; Lou, Yuan; Gao, Dong; Cheng, Xin; Chen, Luonan

doi:10.1016/j.xinn.2022.100364

Cited by 4 publications

(8 citation statements)

References 33 publications

(54 reference statements)

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“…A biological process can be seen as a dynamic process with gene expressions as variables [ 34 , 35 ]. Even in a single cell, the gene expression is always changing with time and cell state, which results in the difficulty to quantify cell heterogeneity and CCC.…”

Section: Discussionmentioning

confidence: 99%

Revealing cell–cell communication pathways with their spatially coupled gene programs

Zhu,

Dai,

Chen

2024

Briefings in Bioinformatics

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Inference of cell–cell communication (CCC) provides valuable information in understanding the mechanisms of many important life processes. With the rise of spatial transcriptomics in recent years, many methods have emerged to predict CCCs using spatial information of cells. However, most existing methods only describe CCCs based on ligand–receptor interactions, but lack the exploration of their upstream/downstream pathways. In this paper, we proposed a new method to infer CCCs, called Intercellular Gene Association Network (IGAN). Specifically, it is for the first time that we can estimate the gene associations/network between two specific single spatially adjacent cells. By using the IGAN method, we can not only infer CCCs in an accurate manner, but also explore the upstream/downstream pathways of ligands/receptors from the network perspective, which are actually exhibited as a new panoramic cell-interaction-pathway graph, and thus provide extensive information for the regulatory mechanisms behind CCCs. In addition, IGAN can measure the CCC activity at single cell/spot resolution, and help to discover the CCC spatial heterogeneity. Interestingly, we found that CCC patterns from IGAN are highly consistent with the spatial microenvironment patterns for each cell type, which further indicated the accuracy of our method. Analyses on several public datasets validated the advantages of IGAN.

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Section: Discussionmentioning

confidence: 99%

Revealing cell–cell communication pathways with their spatially coupled gene programs

Zhu,

Dai,

Chen

2024

Briefings in Bioinformatics

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“…Many diseases result from systematic failures [ 14 , 15 ] and the aging of multiple organs rather than one [ 3 ]. The key switches and master regulators are hubs for stem cell medicine [ 16 ], with early warning signals of the diseases’ tipping points detecting imminent critical transitions [ 17 ].…”

Section: Stem Cell Medicine: Updated Research Highlightsmentioning

confidence: 99%

“…Vitamin D receptor (Daf-12) as an evolutionarily conserved upstream master regulator for both Mi-2/LET-418 and mTOR/LET-363, acts as a “capacitor” like Hsp90 (chaperone-mediated autophagy) [ 25 ]. Detecting degeneration during the early phases of age-related diseases could potentially stop or delay them [ 17 ], including cancer. Potentially rejuvenating aged stem cells [ 26 ] through combining the use of chemical molecules, such as rapalogs, metformin, senolytics, nicotinamide riboside (NR), nicotinamide mononucleotide (NMN), histone deacetylase (HDAC) inhibitors and exogenous stem cells [ 10 ] or stem cell derivatives [ 8 ] and the like [ 9 ], could maximize the positive effects of each individual component while minimizing the side effects of drugs with the potential of “hit-and-run” style.…”

Section: Key Issues Opportunities and Advancesmentioning

confidence: 99%

Optimizing health-span: advances in stem cell medicine and longevity research

Zhang,

Chen,

Huang

2023

Medical Review

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“…From a scRNA-seq dataset of mouse hematopoietic stem cells, a DNB analysis was able to distinguish three different types of transcriptomic variations that corresponded to different cell fates [ 31 ]. Strikingly, Li et al recently reported a module-based DNB (M-DNB) model used for cell fate determination during the differentiation process in human embryonic stem cells (hESCs) [ 32 ]. This M-DNB model, which transforms gene expression information into gene modules/networks based on a protein–protein interaction network, is optimized for scRNA-seq datasets, which often contain higher levels of transcript amplification noise and drop-out events.…”

Section: Dynamical Network Biomarkers Theorymentioning

confidence: 99%

“…This M-DNB model, which transforms gene expression information into gene modules/networks based on a protein–protein interaction network, is optimized for scRNA-seq datasets, which often contain higher levels of transcript amplification noise and drop-out events. Using this model, they identified the key regulators of hESC differentiation as the M-DNB factors, including FOS , HSF1 , MYCN , TP53 , and MYC , with FOS being activated in the first tipping point and MYC in the second [ 32 ]. There are common biological features between ESCs and cancer stem cells (CSCs) [ 38 ].…”

Section: Dynamical Network Biomarkers Theorymentioning

confidence: 99%

New Possibilities for Evaluating the Development of Age-Related Pathologies Using the Dynamical Network Biomarkers Theory

Akagi,

Koizumi,

Kadowaki

et al. 2023

Cells

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Aging is the slowest process in a living organism. During this process, mortality rate increases exponentially due to the accumulation of damage at the cellular level. Cellular senescence is a well-established hallmark of aging, as well as a promising target for preventing aging and age-related diseases. However, mapping the senescent cells in tissues is extremely challenging, as their low abundance, lack of specific markers, and variability arise from heterogeneity. Hence, methodologies for identifying or predicting the development of senescent cells are necessary for achieving healthy aging. A new wave of bioinformatic methodologies based on mathematics/physics theories have been proposed to be applied to aging biology, which is altering the way we approach our understand of aging. Here, we discuss the dynamical network biomarkers (DNB) theory, which allows for the prediction of state transition in complex systems such as living organisms, as well as usage of Raman spectroscopy that offers a non-invasive and label-free imaging, and provide a perspective on potential applications for the study of aging.

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Dynamic network biomarker factors orchestrate cell-fate determination at tipping points during hESC differentiation

Cited by 4 publications

References 33 publications

Revealing cell–cell communication pathways with their spatially coupled gene programs

Revealing cell–cell communication pathways with their spatially coupled gene programs

Optimizing health-span: advances in stem cell medicine and longevity research

New Possibilities for Evaluating the Development of Age-Related Pathologies Using the Dynamical Network Biomarkers Theory

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