Single-Cell Transcriptomic Analysis of mIHC Images via Antigen Mapping

Govek, Kiya W.; Troisi, Emma C.; Woodhouse, Steven; Cámara, Pablo G.

doi:10.1101/672501

Cited by 14 publications

(19 citation statements)

References 51 publications

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“…Given these assumptions, we evaluated whether is able to detect interactions occurring between cluster of cells spatially close. For this reason, we used the high quality CITE-seq dataset described in Govek et al, where the spatial architecture of murine splenic cells was resolved (Govek et al, 2019).…”

Section: Resultsmentioning

confidence: 99%

“…We presented a comprehensive map of active tumor-host interactions in glioma ( Figure S8 ). We have first shown that scTHI can identify recently discovered interactions validated by CODEX (Govek et al, 2019) and then explored common ligand-receptor cross-talk in glioma. Our results confirm, using a much larger scale, that myeloid cells make up the bulk of the microenvironment in glioma and that the ratio between macrophages and microglia cell increases with more aggressive phenotypes as has been previously observed for IDH-mutant glioma (Venteicher et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

“…Single-cell RNA sequencing is the reference technology for the quantification and phenotyping of tumor microenvironment at high resolution (Azizi et al, 2018; Svensson et al, 2018) allowing to measure the composition of individual immune/stromal compartments making up the microenvironment. This technique can be also used for a better elucidation of the tumor-host signalling mechanisms (Kumar et al, 2018) and the identification of tissue-specific interactions at an unprecedented spatially-solved level of details (Govek et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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A MAP of tumor-host interactions in glioma at single cell resolution

Caruso

Garofano

D’Angelo

et al. 2019

Preprint

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Single-cell RNA sequencing is the reference technique to characterize the heterogeneity of tumor microenvironment and can be efficiently used to discover cross-talk mechanisms between immune cells and cancer cells. We present a novel method, single cell Tumor-Host Interaction tool (), to identify significantly activated ligand-receptor interactions across clusters of cells from single-cell RNA sequencing data. We apply our approach to uncover the ligand-receptor interactions in glioma using six publicly available human glioma datasets encompassing 71 patients. We provide a comprehensive map of the signalling mechanisms between malignant cells and non-malignant cells in glioma uncovering potential novel therapeutic targets.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A MAP of tumor-host interactions in glioma at single cell resolution

Caruso

Garofano

D’Angelo

et al. 2019

Preprint

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“…Two recent publications used negative binomial 8 and Gaussian mixture 9 models to identify protein-specific negative "noise" populations. These mixture models were fitted to the counts for each protein, while we used empty droplets to account for protein-specific background and mixture models to fit counts from all proteins within each droplet/cell to infer the technical component reflective of library size (Figs 1B-C).…”

Section: Discussionmentioning

confidence: 99%

Normalizing and denoising protein expression data from droplet-based single cell profiling

Mulè

Martins

Tsang

2020

Preprint

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Recent methods enable simultaneous measurement of protein expression with the transcriptome in single cells by combining protein labeling with DNA barcoded antibodies followed by droplet based single cell capture and sequencing (e.g. CITE-seq). While data normalization and denoising have received considerable attention for single cell RNA-seq data, such methods for protein data have been less explored. Here we showed that a major source of noise in CITE-seq data originated from unbound antibody encapsulated in droplets. We also found that the counts of isotype controls and those of the "negative" population inferred from all protein counts of each cell are significantly correlated, suggesting that their covariation likely reflects cell-to-cell differences due to technical factors such as non-specific antibody binding and droplet-to-droplet differences in capture efficiency of the DNA tags. Motivated by these observations, we developed a normalization method for CITE-seq protein expression data called Denoised and Scaled by Background (DSB). DSB corrects for 1) protein-specific background noise as reflected by empty droplets, 2) the technical cell-to-cell variation as captured by the latent noise component described above. DSB normalization improves separation between positive and negative populations for each protein, centers the negative-staining population around zero, and can improve unbiased protein expression-based clustering. DSB is available through the open source R package "DSB" via a single function call and can be readily integrated with existing single cell analysis workflows, including those in Bioconductor and Seurat.

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“…Single-cell RNA sequencing is the reference technology for the quantification and phenotyping of the tumor microenvironment at high resolution [ 12 , 13 ], enabling measurement of the composition of individual immune/stromal compartments making up the microenvironment. This technique can also be used for a better elucidation of the tumor–host signaling mechanisms [ 14 ] and the identification of tissue-specific interactions at an unprecedented spatially resolved level of detail [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

A map of tumor–host interactions in glioma at single-cell resolution

et al. 2020

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Background Single-cell RNA sequencing is the reference technique for characterizing the heterogeneity of the tumor microenvironment. The composition of the various cell types making up the microenvironment can significantly affect the way in which the immune system activates cancer rejection mechanisms. Understanding the cross-talk signals between immune cells and cancer cells is of fundamental importance for the identification of immuno-oncology therapeutic targets. Results We present a novel method, single-cell Tumor–Host Interaction tool (scTHI), to identify significantly activated ligand–receptor interactions across clusters of cells from single-cell RNA sequencing data. We apply our approach to uncover the ligand–receptor interactions in glioma using 6 publicly available human glioma datasets encompassing 57,060 gene expression profiles from 71 patients. By leveraging this large-scale collection we show that unexpected cross-talk partners are highly conserved across different datasets in the majority of the tumor samples. This suggests that shared cross-talk mechanisms exist in glioma. Conclusions Our results provide a complete map of the active tumor–host interaction pairs in glioma that can be therapeutically exploited to reduce the immunosuppressive action of the microenvironment in brain tumor.

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Single-Cell Transcriptomic Analysis of mIHC Images via Antigen Mapping

Cited by 14 publications

References 51 publications

A MAP of tumor-host interactions in glioma at single cell resolution

A MAP of tumor-host interactions in glioma at single cell resolution

Normalizing and denoising protein expression data from droplet-based single cell profiling

A map of tumor–host interactions in glioma at single-cell resolution

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