Deconvolution algorithms for inference of the cell-type composition of the spatial transcriptome

Zhang, Yingkun; Lin, Xinrui; Yao, Zhixian; Sun, Di; Lin, Xiaochen; Wang, Xiaoyu; Yang, Chaoyong; Song, Jia

doi:10.1016/j.csbj.2022.12.001

Cited by 22 publications

(9 citation statements)

References 58 publications

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“…ST deconvolution method development and assessment have considerations beyond that relevant in deconvolution of bulk RNA-seq data, including modeling of sparse (near single-cell) sequencing data, incorporating number of cells obtained from a matched histology image as a prior constraint, and sharing information across a spatial neighborhood. Others have reviewed 69 and benchmarked 70 methods in this field. We envision that the approaches outlined herein could be used in future studies as a blueprint to assess deconvolution algorithms tuned to ST data.…”

Section: Discussionmentioning

confidence: 99%

Community assessment of methods to deconvolve cellular composition from bulk gene expression

White

Reyniès

Newman

et al. 2022

Preprint

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Deconvolution methods infer levels of immune and stromal infiltration from bulk expression of tumor samples. These methods allow projection of characteristics of the tumor microenvironment, known to affect patient outcome and therapeutic response, onto the millions of bulk transcriptional profiles in public databases, many focused on uniquely valuable and clinically-annotated cohorts. Despite the wide development of such methods, a standardized dataset with ground truth to evaluate their performance has been lacking. We generated and sequenced in vitro and in silico admixtures of tumor, immune, and stromal cells and used them as ground truth in a community-wide DREAM Challenge that provided an objective, unbiased assessment of six widely-used published deconvolution methods and of 22 new analytical approaches developed by international teams. Our results demonstrate that existing methods predict many cell types well, while team-contributed methods highlight the potential to resolve functional states of T cells that were either not covered by published reference signatures or estimated poorly by some published methods. Our assessment and the open-source implementations of top-performing methods will allow researchers to apply the deconvolution approach most appropriate to querying their cell type of interest. Further, our publicly-available admixed and purified expression profiles will be a valuable resource to those developing deconvolution methods, including in non-malignant settings involving immune cells.

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Section: Discussionmentioning

confidence: 99%

Community assessment of methods to deconvolve cellular composition from bulk gene expression

White

Reyniès

Newman

et al. 2022

Preprint

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“…We used the “SPOTlight” package in R to integrate and analyze spatial transcriptome sequencing (ST‐seq) data and scRNA‐seq data, as described earlier. Deconvolution analysis, refers to the computational techniques aimed at estimating the proportions of different cell types in heterogeneous mixture samples, 16,17 was performed using SPOTlight analysis as reported by Elosua‐Bayes et al 18 . Briefly, the signature proportion of the selected scRNA‐seq cell type was equal to the sum of the proportions of each cell type in the different regions, divided by the sum of the proportions of that cell type in all spots 19 …”

Section: Methodsmentioning

confidence: 99%

“…We used the "SPOTlight" package in R to integrate and analyze spatial transcriptome sequencing (ST-seq) data and scRNA-seq data, as described earlier. Deconvolution analysis, refers to the computational techniques aimed at estimating the proportions of different cell types in heterogeneous mixture samples, 16,17 was performed using SPOTlight analysis as reported by Elosua-Bayes et al 18 Briefly, the signature proportion of the selected scRNA-seq cell type was equal to the sum of the proportions of each cell type in the different regions, divided by the sum of the proportions of that cell type in all spots. 19 2.7 | Analysis of transcriptomic data from TCGA and the Genotype-Tissue Expression (GTEx) portal RNA-seq data from TCGA and GTEx in transcripts-permillion format and the corresponding clinical information, which was uniformly processed using Toil software, were downloaded from UCSC Xena (https:// xenab rowser.…”

Section: Spotlight Analysismentioning

confidence: 99%

MIF as a potential diagnostic and prognostic biomarker for triple‐negative breast cancer that correlates with the polarization of M2 macrophages

Chen,

Liu,

Hong

et al. 2024

The FASEB Journal

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Macrophage migration inhibitory factor (MIF) is a proinflammatory cytokine that plays a crucial role in antitumor immunity. However, the role of MIF in influencing the tumor microenvironment (TME) and prognosis of triple‐negative breast cancer (TNBC) remains to be elucidated. Using R, we analyzed single‐cell RNA sequencing (scRNA‐seq) data of 41 567 cells from 10 TNBC tumor samples and spatial transcriptomic data from two patients. Relationships between MIF expression and immune cell infiltration, clinicopathological stage, and survival prognosis were determined using samples from The Cancer Genome Atlas (TCGA) and validated in a clinical cohort using immunohistochemistry. Analysis of scRNA‐seq data revealed that MIF secreted by epithelial cells in TNBC patients could regulate the polarization of macrophages into the M2 phenotype, which plays a key role in modulating the TME. Spatial transcriptomic data also showed that epithelial cells (tumor cells) and MIF were proximally located. Analysis of TCGA samples confirmed that tumor tissues of patients with high MIF expression were enriched with M2 macrophages and showed a higher T stage. High MIF expression was significantly associated with poor patient prognosis. Immunohistochemical staining showed high MIF expression was associated with younger patients and worse clinicopathological staging. MIF secreted by epithelial cells may represent a potential biomarker for the diagnosis and prognosis of TNBC and may promote TNBC invasion by remodeling the tumor immune microenvironment.

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“…The current 10x Visium methodology incorporates spot sizes of 55 μm, which falls short of single-cell resolution. To improve the resolution, separate conventional (non-spatial) scRNA-seq data can be integrated with the spatial data, and combined with computational deconvolution methods, used to estimate single cell contributions (see reviews ( Melo Ferreira et al, 2021 ; Rao et al, 2021 ; Zhang et al, 2023 ). Studies incorporating spatial transcriptomic profiling methods on human kidney samples are emerging, with examples including AKI to understand immune cell infiltration in histological context ( Melo Ferreira et al, 2021 ), development of an atlas across multiple kidney diseases ( Lake et al, 2021 ), cell-mediated rejection in kidney transplantation ( Salem et al, 2022 ), and small RNA involvement in FSGS ( Williams et al, 2022 ).…”

Section: Therapeutic Development For Kidney Disease In Single Cell Eramentioning

confidence: 99%

Multi-omic single cell sequencing: Overview and opportunities for kidney disease therapeutic development

Pregizer

Vreven

Mathur

et al. 2023

Front. Mol. Biosci.

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Single cell sequencing technologies have rapidly advanced in the last decade and are increasingly applied to gain unprecedented insights by deconstructing complex biology to its fundamental unit, the individual cell. First developed for measurement of gene expression, single cell sequencing approaches have evolved to allow simultaneous profiling of multiple additional features, including chromatin accessibility within the nucleus and protein expression at the cell surface. These multi-omic approaches can now further be applied to cells in situ, capturing the spatial context within which their biology occurs. To extract insights from these complex datasets, new computational tools have facilitated the integration of information across different data types and the use of machine learning approaches. Here, we summarize current experimental and computational methods for generation and integration of single cell multi-omic datasets. We focus on opportunities for multi-omic single cell sequencing to augment therapeutic development for kidney disease, including applications for biomarkers, disease stratification and target identification.

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Deconvolution algorithms for inference of the cell-type composition of the spatial transcriptome

Cited by 22 publications

References 58 publications

Community assessment of methods to deconvolve cellular composition from bulk gene expression

Community assessment of methods to deconvolve cellular composition from bulk gene expression

MIF as a potential diagnostic and prognostic biomarker for triple‐negative breast cancer that correlates with the polarization of M2 macrophages

Multi-omic single cell sequencing: Overview and opportunities for kidney disease therapeutic development

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