The landscape of cell–cell communication through single-cell transcriptomics

Almet, Axel A.; Cang, Zixuan; Jin, Suoqin

doi:10.1016/j.coisb.2021.03.007

Cited by 130 publications

(141 citation statements)

References 45 publications

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“…We therefore believe that it is essential to establish a benchmark to comprehensively assess the predictive power of CCC methods. However, a gold standard for benchmarking is currently not available and the biological ground truth is largely unknown 3,19 . The field needs to identify experimental settings capable of establishing the biological ground truth.…”

Section: Inference Assumptions and Benchmarkingmentioning

confidence: 99%

“…However, any in silico benchmark is by definition only a simplified approximation of reality, with its own biases 49 . To our knowledge, appropriate benchmarks for resources and methods used in CCC inference are yet to be defined, although some proposals exist 19 , that we elaborate on in Supp. Note 4.…”

Section: Inference Assumptions and Benchmarkingmentioning

confidence: 99%

“…As a consequence of the observed disagreement in the results obtained when using different methods and resources, we argue that an appropriate benchmark is paramount for the future development of the CCC inference field. Some effort has already been directed to the assessment of different methods and specific directions were already proposed 19 . To this end, we also share our current benchmark ideas.…”

Section: Supplementary Note 4 Single-cell CCC Benchmark Directionsmentioning

confidence: 99%

“…Examples: This approach was already used as a way to validate some methods 13,18 , while other methods explicitly take spatial information into account for CCC inference 19 . Another example is confining CCC inference to cells that are expected to be in close contact, e.g.…”

Section: Associations Between CCC Activity and Spatial-adjacencymentioning

confidence: 99%

“…In turn, these different approaches result in diverse scoring systems that are difficult to compare and evaluate. The difficulties are further exacerbated by the lack of an appropriate gold standard to benchmark the performance of CCC methods 3,19 .…”

Section: Introductionmentioning

confidence: 99%

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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: Inference Assumptions and Benchmarkingmentioning

confidence: 99%

Section: Inference Assumptions and Benchmarkingmentioning

confidence: 99%

Section: Supplementary Note 4 Single-cell CCC Benchmark Directionsmentioning

confidence: 99%

Section: Associations Between CCC Activity and Spatial-adjacencymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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CellMag‐CARWash: A High Throughput Droplet Microfluidic Device for Live Cell Isolation and Single Cell Applications

Rupp,

Cook,

Purcell

et al. 2024

Advanced Biology

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The recent push toward understanding an individual cell's behavior and identifying cellular heterogeneity has created an unmet need for technologies that can probe live cells at the single‐cell level. Cells within a population are known to exhibit heterogeneous responses to environmental cues. These differences can lead to varied cellular states, behavior, and responses to therapeutics. Techniques are needed that are not only capable of processing and analyzing cellular populations at the single cell level, but also have the ability to isolate specific cell populations from a complex sample at high throughputs. The new CellMag‐Coalesce‐Attract‐Resegment Wash (CellMag‐CARWash) system combines positive magnetic selection with droplet microfluidic devices to isolate cells of interest from a mixture with >93% purity and incorporate treatments within individual droplets to observe single cell biological responses. This workflow is shown to be capable of probing the single cell extracellular vesicle (EV) secretion of MCF7 GFP cells. This article reports the first measurement of β‐Estradiol's effect on EV secretion from MCF7 cells at the single cell level. Single cell processing revealed that MCF7 GFP cells possess a heterogeneous response to β‐Estradiol stimulation with a 1.8‐fold increase relative to the control.

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Spatially Informed Graph Structure Learning Extracts Insights from Spatial Transcriptomics

Nie,

Yu,

Wang

et al. 2024

Advanced Science

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Embeddings derived from cell graphs hold significant potential for exploring spatial transcriptomics (ST) datasets. Nevertheless, existing methodologies rely on a graph structure defined by spatial proximity, which inadequately represents the diversity inherent in cell‐cell interactions (CCIs). This study introduces STAGUE, an innovative framework that concurrently learns a cell graph structure and a low‐dimensional embedding from ST data. STAGUE employs graph structure learning to parameterize and refine a cell graph adjacency matrix, enabling the generation of learnable graph views for effective contrastive learning. The derived embeddings and cell graph improve spatial clustering accuracy and facilitate the discovery of novel CCIs. Experimental benchmarks across 86 real and simulated ST datasets show that STAGUE outperforms 15 comparison methods in clustering performance. Additionally, STAGUE delineates the heterogeneity in human breast cancer tissues, revealing the activation of epithelial‐to‐mesenchymal transition and PI3K/AKT signaling in specific sub‐regions. Furthermore, STAGUE identifies CCIs with greater alignment to established biological knowledge than those ascertained by existing graph autoencoder‐based methods. STAGUE also reveals the regulatory genes that participate in these CCIs, including those enriched in neuropeptide signaling and receptor tyrosine kinase signaling pathways, thereby providing insights into the underlying biological processes.

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The landscape of cell–cell communication through single-cell transcriptomics

Cited by 130 publications

References 45 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

CellMag‐CARWash: A High Throughput Droplet Microfluidic Device for Live Cell Isolation and Single Cell Applications

Spatially Informed Graph Structure Learning Extracts Insights from Spatial Transcriptomics

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