From measuring noise toward integrated single-cell biology

Paszek, Pawel

doi:10.3389/fgene.2014.00408

Cited by 5 publications

(3 citation statements)

References 43 publications

(51 reference statements)

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“…Such is the case when studying bone marrow hematopoietic progenitors [37], or cell composition and states during an immune response [29]. Importantly, biological interpretation of single-cell expression data is not trivial and critically depends on emerging computational approaches to accurately quantify gene expression, while accounting for technical noise [28,29,31,35,36,[38][39][40]. An emerging challenge is to accurately cluster cells into coherent groups on the basis of their gene expression profiles.…”

Section: The Single-cell Rna Sequencing Revolutionmentioning

confidence: 99%

Each cell counts: Hematopoiesis and immunity research in the era of single cell genomics

Jaitin

Keren‐Shaul

Elefant

et al. 2015

Seminars in Immunology

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Section: The Single-cell Rna Sequencing Revolutionmentioning

confidence: 99%

Each cell counts: Hematopoiesis and immunity research in the era of single cell genomics

Jaitin

Keren‐Shaul

Elefant

et al. 2015

Seminars in Immunology

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“…An ideal tool to integrate unpaired data would be able to work in unsupervised manner, that is it would learn the whole data structure from data themselves. That requires the algorithm to model several confounders including technical noise due to different technologies (13,55), biological noise (56,57) and batch effects (27). For this reason, we tested the ability of MOWGAN to work with multiple samples of unpaired data with or without specifying the sample identity in the training step.…”

Section: Semisupervised Training Improves Results and Unveils Hidden ...mentioning

confidence: 99%

Scalable Integration of Multiomic Single Cell Data Using Generative Adversarial Networks

Giansanti

Giannese

Botrugno

et al. 2023

Preprint

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Single cell profiling has become a common practice to investigate the complexity of tissues, organs and organisms. Recent technological advances are expanding our capabilities to profile various molecular layers beyond the transcriptome such as, but not limited to, the genome, the epigenome and the proteome. Depending on the experimental procedure, these data can be obtained from separate assays or from the very same cells. Despite development of computational methods for data integration is an active research field, most of the available strategies have been devised for the joint analysis of two modalities and cannot accommodate a high number of them. To solve this problem, we here propose a multiomic data integration framework based on Wasserstein Generative Adversarial Networks (MOWGAN) suitable for the analysis of paired or unpaired data with high number of modalities (>2). At the core of our strategy is a single network trained on all modalities together, limiting the computational burden when many molecular layers are evaluated. Source code of our framework is available at https://github.com/vgiansanti/MOWGAN.

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“…Cell-to-cell variability can be observed in the kinetics of signalling reactions [ 175 , 176 ]. Dynamic signalling events are significantly influenced by fundamental physical processes [ 177 ], such as cell cycle phase [ 178 ], growth rate [ 179 ], and the intrinsic promiscuity of protein-protein interactions [ 17 ]. Each of these dynamic signalling events must be properly integrated to determine the appropriate response of a cell to TNF, as apoptotic decision-making has a significant impact on both the individual cell and wider population.…”

Section: Introductionmentioning

confidence: 99%

Cellular heterogeneity in TNF/TNFR1 signalling: live cell imaging of cell fate decisions in single cells

Preedy,

White,

Tergaonkar

2024

Cell Death Dis

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Cellular responses to TNF are inherently heterogeneous within an isogenic cell population and across different cell types. TNF promotes cell survival by activating pro-inflammatory NF-κB and MAPK signalling pathways but may also trigger apoptosis and necroptosis. Following TNF stimulation, the fate of individual cells is governed by the balance of pro-survival and pro-apoptotic signalling pathways. To elucidate the molecular mechanisms driving heterogenous responses to TNF, quantifying TNF/TNFR1 signalling at the single-cell level is crucial. Fluorescence live-cell imaging techniques offer real-time, dynamic insights into molecular processes in single cells, allowing for detection of rapid and transient changes, as well as identification of subpopulations, that are likely to be missed with traditional endpoint assays. Whilst fluorescence live-cell imaging has been employed extensively to investigate TNF-induced inflammation and TNF-induced cell death, it has been underutilised in studying the role of TNF/TNFR1 signalling pathway crosstalk in guiding cell-fate decisions in single cells. Here, we outline the various opportunities for pathway crosstalk during TNF/TNFR1 signalling and how these interactions may govern heterogenous responses to TNF. We also advocate for the use of live-cell imaging techniques to elucidate the molecular processes driving cell-to-cell variability in single cells. Understanding and overcoming cellular heterogeneity in response to TNF and modulators of the TNF/TNFR1 signalling pathway could lead to the development of targeted therapies for various diseases associated with aberrant TNF/TNFR1 signalling, such as rheumatoid arthritis, metabolic syndrome, and cancer.

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From measuring noise toward integrated single-cell biology

Cited by 5 publications

References 43 publications

Each cell counts: Hematopoiesis and immunity research in the era of single cell genomics

Each cell counts: Hematopoiesis and immunity research in the era of single cell genomics

Scalable Integration of Multiomic Single Cell Data Using Generative Adversarial Networks

Cellular heterogeneity in TNF/TNFR1 signalling: live cell imaging of cell fate decisions in single cells

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