A pan-cancer single-cell transcriptional atlas of tumor infiltrating myeloid cells

Cheng, Sen; Li, Ziyi; Gao, Ranran; Xing, Baocai; Gao, Yunong; Yang, Yu; Qin, Siyu; Zhang, Lei; Ouyang, Hanqiang; Du, Peng; Jiang, Liang; Zhang, Bin; Yang, Yue; Wang, Xiliang; Ren, Xianwen; Bei, Jin‐Xin; Hu, Xueda; Bu, Zhaode; Ji, Jiafu; Zhang, Zemin

doi:10.1016/j.cell.2021.01.010

Cited by 654 publications

(771 citation statements)

References 84 publications

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“…As an example, CD4+ T cells and CD8+ T cells, the two most abundant immune cell types found within tumours, were assigned a low CCC communication activity. In terms of the agreement in CCC event predictions we found that only a few biological patterns are robust across many methods, namely the SPP1+ macrophages have been predicted to be main players of CCC signalling, supported by their enrichment in CRC tumour tissues 23 and frequent association with pro-metastatic role 44,45 .…”

Section: Impact Of Methods and Resourcesmentioning

confidence: 89%

Comparison of Resources and Methods to infer Cell-Cell Communication from Single-cell RNA Data

Dimitrov

Türei

Boys

et al. 2021

Preprint

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The growing availability of single-cell data has sparked an increased interest in the inference of cell-cell communication from this data. Many tools have been developed for this purpose. Each of them consists of a resource of intercellular interactions prior knowledge and a method to predict potential cell-cell communication events. Yet the impact of the choice of resource and method on the resulting predictions is largely unknown. To shed light on this, we created a framework, available at https://github.com/saezlab/ligrec_decoupler, to facilitate a comparative assessment of methods for inferring cell-cell communication from single cell transcriptomics data and then compared 15 resources and 6 methods. We found few unique interactions and a varying degree of overlap among the resources, and observed uneven coverage in terms of pathways and biological categories. We analysed a colorectal cancer single cell RNA-Seq dataset using all possible combinations of methods and resources. We found major differences among the highest ranked intercellular interactions inferred by each method even when using the same resources. The varying predictions lead to fundamentally different biological interpretations, highlighting the need to benchmark resources and methods.

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Section: Impact Of Methods and Resourcesmentioning

confidence: 89%

Comparison of Resources and Methods to infer Cell-Cell Communication from Single-cell RNA Data

Dimitrov

Türei

Boys

et al. 2021

Preprint

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show abstract

“…Second, tumor cells consistently show a patient-specific expression pattern, whereas immune and stromal infiltrates are more homogenous across different subjects (98)(99)(100)(101). Third, both innate and adaptive immunity are involved in cancer pathogenesis (102,103). Fourth, individual cellular components crosstalk with each other and form intricate interaction networks (104).…”

Section: Potential Insights From Single-cell Analysis Tumor Multicellular Ecosystemmentioning

confidence: 99%

Immunotherapy in the Treatment of Urothelial Bladder Cancer: Insights From Single-Cell Analysis

Zang

Yang

et al. 2021

Front. Oncol.

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Urothelial bladder cancer (UBC) is a global challenge of public health with limited therapeutic options. Although the emergence of cancer immunotherapy, most notably immune checkpoint inhibitors, represents a major breakthrough in the past decade, many patients still suffer from unsatisfactory clinical outcome. A thorough understanding of the fundamental cellular and molecular mechanisms responsible for antitumor immunity may lead to optimized treatment guidelines and new immunotherapeutic strategies. With technological developments and protocol refinements, single-cell approaches have become powerful tools that provide unprecedented insights into the kaleidoscopic tumor microenvironment and intricate cell-cell communications. In this review, we summarize recent applications of single-cell analysis in characterizing the UBC multicellular ecosystem, and discuss how to leverage the high-resolution information for more effective immune-based therapies.

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“…ScRNAseq has generated comprehensive compendiums of cell types per tissue and identified cell types that play a critical role in genetic diseases, which can guide cell type-specific investigations (The Tabula Muris Consortium et al, 2018;Aizarani et al, 2019). In particular, scRNAseq has already been used as a powerful tool in fields such as immunology (Mizoguchi et al, 2018;Zhang, 2019), cancer (Cheng et al, 2021;Velten et al, 2021), and neurological diseases (Jäkel et al, 2019;Mathys et al, 2019). ScRNAseq of diseased tissues or patient-specific iPSCs could further advance the precision medicine approach in the field of rare genetic disorders (Choi et al, 2020).…”

Section: Limitations and Future Perspectives Of Rnaseq In The Molecular Diagnosis Of Rare Disordersmentioning

confidence: 99%

How Machine Learning and Statistical Models Advance Molecular Diagnostics of Rare Disorders Via Analysis of RNA Sequencing Data

Schlieben

Prokisch

Yépez

2021

Front. Mol. Biosci.

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Rare diseases, although individually rare, collectively affect approximately 350 million people worldwide. Currently, nearly 6,000 distinct rare disorders with a known molecular basis have been described, yet establishing a specific diagnosis based on the clinical phenotype is challenging. Increasing integration of whole exome sequencing into routine diagnostics of rare diseases is improving diagnostic rates. Nevertheless, about half of the patients do not receive a genetic diagnosis due to the challenges of variant detection and interpretation. During the last years, RNA sequencing is increasingly used as a complementary diagnostic tool providing functional data. Initially, arbitrary thresholds have been applied to call aberrant expression, aberrant splicing, and mono-allelic expression. With the application of RNA sequencing to search for the molecular diagnosis, the implementation of robust statistical models on normalized read counts allowed for the detection of significant outliers corrected for multiple testing. More recently, machine learning methods have been developed to improve the normalization of RNA sequencing read count data by taking confounders into account. Together the methods have increased the power and sensitivity of detection and interpretation of pathogenic variants, leading to diagnostic rates of 10–35% in rare diseases. In this review, we provide an overview of the methods used for RNA sequencing and illustrate how these can improve the diagnostic yield of rare diseases.

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A pan-cancer single-cell transcriptional atlas of tumor infiltrating myeloid cells

Cited by 654 publications

References 84 publications

Comparison of Resources and Methods to infer Cell-Cell Communication from Single-cell RNA Data

Comparison of Resources and Methods to infer Cell-Cell Communication from Single-cell RNA Data

Immunotherapy in the Treatment of Urothelial Bladder Cancer: Insights From Single-Cell Analysis

How Machine Learning and Statistical Models Advance Molecular Diagnostics of Rare Disorders Via Analysis of RNA Sequencing Data

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