Single‐Cell Multi‐Omics and Its Prospective Application in Cancer Biology

Peng, Anghui; Mao, Xiying; Zhong, Jiawei; Fan, Shuangshuang; Hu, Youjin

doi:10.1002/pmic.201900271

Cited by 35 publications

(29 citation statements)

References 67 publications

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“…research to study individual heterogeneity, drug resistance, or disease progression at an unprecedented level (47,48). Especially, T cell focused immunological studies will benefit from recent developments as newly arising techniques can also simultaneously reconstruct TCR sequences and determine their specificities for a predefined set of epitopes (49)(50)(51).…”

Section: Defining Tissue-specific Treg Characteristics Using Single-cell Multi-omics Integrationmentioning

confidence: 99%

Antigen-Specific Treg Therapy in Type 1 Diabetes – Challenges and Opportunities

et al. 2021

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Regulatory T cells (Tregs) are key mediators of peripheral self-tolerance and alterations in their frequencies, stability, and function have been linked to autoimmunity. The antigen-specific induction of Tregs is a long-envisioned goal for the treatment of autoimmune diseases given reduced side effects compared to general immunosuppressive therapies. However, the translation of antigen-specific Treg inducing therapies for the treatment or prevention of autoimmune diseases into the clinic remains challenging. In this mini review, we will discuss promising results for antigen-specific Treg therapies in allergy and specific challenges for such therapies in autoimmune diseases, with a focus on type 1 diabetes (T1D). We will furthermore discuss opportunities for antigen-specific Treg therapies in T1D, including combinatorial strategies and tissue-specific Treg targeting. Specifically, we will highlight recent advances in miRNA-targeting as a means to foster Tregs in autoimmunity. Additionally, we will discuss advances and perspectives of computational strategies for the detailed analysis of tissue-specific Tregs on the single-cell level.

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Section: Defining Tissue-specific Treg Characteristics Using Single-cell Multi-omics Integrationmentioning

confidence: 99%

Antigen-Specific Treg Therapy in Type 1 Diabetes – Challenges and Opportunities

et al. 2021

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“…However, if we can assume that cellular heterogeneity plays a critical role in tissue behavior, omic data at single-cell resolution are needed and are within the reach of current technologies. Single-cell multiomic analyses have been most actively conducted in the clinical field of cancer and hematological diseases and have been successfully used in clarifying many unknown features of their etiologies [ 15 , 22 – 26 ].…”

Section: Multi-scale Structure Of the Biological System: Omic Measurement At Single Granularity May Uncover Hidden Aspects Of Transition mentioning

confidence: 99%

Multiomic technologies for analyses of inborn errors of immunity: from snapshot of the average cell to dynamic temporal picture at single-cell resolution

2021

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Advances in DNA sequencing technology have significantly impacted human genetics; they have enabled the analysis of genetic causes of rare diseases, which are usually pathogenic variants in a single gene at the nucleotide sequence level. However, since the quantity of data regarding the relationship between genotype and phenotype is insufficient to diagnose some rare immune diseases definitively, genetic information alone cannot help obtain a mechanistic understanding of the disease etiology. For such cases, exploring the molecular phenotype using multiomic analyses could be the approach of choice. In this review, we first overview current technologies for multiomic analysis, particularly focusing on RNA and protein profiling of bulk cell ensembles. We then discuss the measurement modality and granularity issue because it is critical to design multiomic experiments properly. Next, we illustrate the importance of bioimaging by describing our experience with the analysis of an autoinflammatory disease, cryopyrin-associated periodic fever syndrome, which could be caused by low-frequency somatic mosaicism and cannot be well characterized only by multiomic snapshot analyses of an ensemble of many immune cells. We found it powerful to complement the multiomic data with bioimaging data that can provide us with indispensable time-specific dynamic information of every single cell in the “immune cell society.” Because we now have many measurement tools in different modalities and granularity to tackle the etiology of rare hereditary immune diseases, we might gain a deeper understanding of the pathogenic mechanisms of these diseases by taking full advantage of these tools in an integrated manner.

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“…Multi-omics integration studies and network-based models use sophisticated computational approaches to integrate data obtained by different high-throughput omics technologies [8,37,59]. These approaches explore interactions between different levels of alterations (DNA, RNA, protein, chromatin, and so on) to evaluate systems-level changes in tumor cells and identify integrated molecular signatures that correlate with different aspects of tumorigenesis, patient outcome, and drug response.…”

Section: Functional Exploration Of Integrated Multi-omics Analyses Anmentioning

confidence: 99%

Strategies for Functional Interrogation of Big Cancer Data Using Drosophila Cancer Models

Bangi¹

2020

IJMS

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Rapid development of high throughput genome analysis technologies accompanied by significant reduction in costs has led to the accumulation of an incredible amount of data during the last decade. The emergence of big data has had a particularly significant impact in biomedical research by providing unprecedented, systems-level access to many disease states including cancer, and has created promising opportunities as well as new challenges. Arguably, the most significant challenge cancer research currently faces is finding effective ways to use big data to improve our understanding of molecular mechanisms underlying tumorigenesis and developing effective new therapies. Functional exploration of these datasets and testing predictions from computational approaches using experimental models to interrogate their biological relevance is a key step towards achieving this goal. Given the daunting scale and complexity of the big data available, experimental systems like Drosophila that allow large-scale functional studies and complex genetic manipulations in a rapid, cost-effective manner will be of particular importance for this purpose. Findings from these large-scale exploratory functional studies can then be used to formulate more specific hypotheses to be explored in mammalian models. Here, I will discuss several strategies for functional exploration of big cancer data using Drosophila cancer models.

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Single‐Cell Multi‐Omics and Its Prospective Application in Cancer Biology

Cited by 35 publications

References 67 publications

Antigen-Specific Treg Therapy in Type 1 Diabetes – Challenges and Opportunities

Antigen-Specific Treg Therapy in Type 1 Diabetes – Challenges and Opportunities

Multiomic technologies for analyses of inborn errors of immunity: from snapshot of the average cell to dynamic temporal picture at single-cell resolution

Strategies for Functional Interrogation of Big Cancer Data Using Drosophila Cancer Models

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