Mitophagy and clear cell renal cell carcinoma: insights from single-cell and spatial transcriptomics analysis DOI Creative Commons
Lai Jiang,

Xing Ren,

Jinyan Yang

et al.

Frontiers in Immunology, Journal Year: 2024, Volume and Issue: 15

Published: June 27, 2024

Background Clear Cell Renal Carcinoma (ccRCC) is the most common type of kidney cancer, characterized by high heterogeneity and complexity. Recent studies have identified mitochondrial defects autophagy as key players in development ccRCC. This study aims to delve into changes mitophagic activity within ccRCC its impact on tumor microenvironment, revealing role cell metabolism, development, survival strategies. Methods Comprehensive analysis tissues using single sequencing spatial transcriptomics reveal mitophagy Mitophagy was determined be altered among renal clear cells gene set scoring. Key populations prognostic genes were NMF approaches. The UBB also demonstrated vitro experiments. Results Compared normal tissue, various types exhibited significantly increased levels mitophagy, especially cells. associated with levels, such UBC, UBA52, TOMM7, UBB, MAP1LC3B, CSNK2B, identified, their expression closely linked poor patient prognosis. Particularly, ubiquitination process involving found crucial for quality control. Conclusion highlights central regulatory factors ccRCC, significance disease progression.

Language: Английский

Best practices for single-cell analysis across modalities DOI Open Access
Lukas Heumos, Anna C. Schaar, Christopher Lance

et al.

Nature Reviews Genetics, Journal Year: 2023, Volume and Issue: 24(8), P. 550 - 572

Published: March 31, 2023

Language: Английский

Citations

535
Spatial multi-omic map of human myocardial infarction DOI Open Access
Christoph Kuppe, Ricardo O. Ramirez Flores, Zhijian Li

et al.

Nature, Journal Year: 2022, Volume and Issue: 608(7924), P. 766 - 777

Published: Aug. 10, 2022

Language: Английский

Citations

390
Screening cell–cell communication in spatial transcriptomics via collective optimal transport DOI Creative Commons
Zixuan Cang, Yanxiang Zhao, Axel A. Almet

et al.

Nature Methods, Journal Year: 2023, Volume and Issue: 20(2), P. 218 - 228

Published: Jan. 23, 2023

Abstract Spatial transcriptomic technologies and spatially annotated single-cell RNA sequencing datasets provide unprecedented opportunities to dissect cell–cell communication (CCC). However, incorporation of the spatial information complex biochemical processes required in reconstruction CCC remains a major challenge. Here, we present COMMOT (COMMunication analysis by Optimal Transport) infer transcriptomics, which accounts for competition between different ligand receptor species as well distances cells. A collective optimal transport method is developed handle molecular interactions constraints. Furthermore, introduce downstream tools signaling directionality genes regulated using machine learning models. We apply simulation data eight acquired with five show its effectiveness robustness identifying varying resolutions gene coverages. Finally, identifies new CCCs during skin morphogenesis case study human epidermal development.

Language: Английский

Citations

207
Gene regulatory network inference in the era of single-cell multi-omics DOI
Pau Badia-i-Mompel, Lorna Wessels, Sophia Müller‐Dott

et al.

Nature Reviews Genetics, Journal Year: 2023, Volume and Issue: 24(11), P. 739 - 754

Published: June 26, 2023

Language: Английский

Citations

189
Computational solutions for spatial transcriptomics DOI Creative Commons
Iivari Kleino,

Paulina Frolovaitė,

Tomi Suomi

et al.

Computational and Structural Biotechnology Journal, Journal Year: 2022, Volume and Issue: 20, P. 4870 - 4884

Published: Jan. 1, 2022

Transcriptome level expression data connected to the spatial organization of cells and molecules would allow a comprehensive understanding how gene is structure function in biological systems. The transcriptomics platforms may soon provide such information. However, current still lack resolution, capture only fraction transcriptome heterogeneity, or throughput for large scale studies. strengths weaknesses ST computational solutions need be taken into account when planning basis analysis developed single-cell RNA-sequencing data, with advancements taking connectedness transcriptomes. scRNA-seq tools are modified new like deep learning-based joint expression, spatial, image extract information spatially resolved can reveal remarkable insights patterns cell signaling, type variations connection type-specific signaling complex tissues. This review covers topics that help choosing platform research. We focus on currently available methods their limitations. Of solutions, we an overview steps used analysis. compatibility types provided by frameworks summarized.

Language: Английский

Citations

87
Spatial omics technologies at multimodal and single cell/subcellular level DOI Creative Commons
Jiwoon Park, Junbum Kim,

Tyler Lewy

et al.

Genome biology, Journal Year: 2022, Volume and Issue: 23(1)

Published: Dec. 13, 2022

Abstract Spatial omics technologies enable a deeper understanding of cellular organizations and interactions within tissue interest. These assays can identify specific compartments or regions in with differential transcript protein abundance, delineate their interactions, complement other methods defining phenotypes. A variety spatial methodologies are being developed commercialized; however, these techniques differ resolution, multiplexing capability, scale/throughput, coverage. Here, we review the current prospective landscape single cell to subcellular resolution analysis tools provide comprehensive picture for both research clinical applications.

Language: Английский

Citations

78
Context-aware deconvolution of cell–cell communication with Tensor-cell2cell DOI Creative Commons
Erick Armingol, Hratch Baghdassarian, Cameron Martino

et al.

Nature Communications, Journal Year: 2022, Volume and Issue: 13(1)

Published: June 27, 2022

Abstract Cell interactions determine phenotypes, and intercellular communication is shaped by cellular contexts such as disease state, organismal life stage, tissue microenvironment. Single-cell technologies measure the molecules mediating cell–cell communication, emerging computational tools can exploit these data to decipher communication. However, current methods either disregard context or rely on simple pairwise comparisons between samples, thus limiting ability complex across multiple time points, levels of severity, spatial contexts. Here we present Tensor-cell2cell, an unsupervised method using tensor decomposition, which deciphers context-driven simultaneously accounting for stages, states, locations cells. To do so, Tensor-cell2cell uncovers patterns associated with different phenotypic states determined unique combinations cell types ligand-receptor pairs. As such, robustly improves upon extends analytical capabilities existing tools. We show identify modules distinct processes (e.g., participating pairs) linked severities Coronavirus Disease 2019 Autism Spectrum Disorder. Thus, introduce effective easy-to-use strategy understanding diverse conditions.

Language: Английский

Citations

71
Decoding the tumor microenvironment with spatial technologies DOI
Logan A. Walsh,

Daniela F. Quail

Nature Immunology, Journal Year: 2023, Volume and Issue: 24(12), P. 1982 - 1993

Published: Nov. 27, 2023

Language: Английский

Citations

66
The diversification of methods for studying cell–cell interactions and communication DOI
Erick Armingol, Hratch Baghdassarian, Nathan E. Lewis

et al.

Nature Reviews Genetics, Journal Year: 2024, Volume and Issue: 25(6), P. 381 - 400

Published: Jan. 18, 2024

Language: Английский

Citations

57
SRTsim: spatial pattern preserving simulations for spatially resolved transcriptomics DOI Creative Commons
Jiaqiang Zhu, Lulu Shang, Xiang Zhou

et al.

Genome biology, Journal Year: 2023, Volume and Issue: 24(1)

Published: March 3, 2023

Spatially resolved transcriptomics (SRT)-specific computational methods are often developed, tested, validated, and evaluated in silico using simulated data. Unfortunately, existing SRT data poorly documented, hard to reproduce, or unrealistic. Single-cell simulators not directly applicable for simulation as they cannot incorporate spatial information. We present SRTsim, an SRT-specific simulator scalable, reproducible, realistic simulations. SRTsim only maintains various expression characteristics of but also preserves patterns. illustrate the benefits benchmarking clustering, pattern detection, cell-cell communication identification.

Language: Английский

Citations

47