Cell-specific priors rescue differential gene expression in spatial spot-based technologies DOI Creative Commons
Ornit Nahman,

Timothy J Few-Cooper,

Shai S. Shen-Orr

и другие.

Briefings in Bioinformatics, Год журнала: 2024, Номер 26(1)

Опубликована: Ноя. 22, 2024

Abstract Spatial transcriptomics (ST), a breakthrough technology, captures the complex structure and state of tissues through spatial profiling gene expression. A variety ST technologies have now emerged, most prominently spot-based platforms such as Visium. Despite widespread use its distinct data characteristics, vast majority studies continue to analyze using algorithms originally designed for older single-cell (SC) bulk RNA-seq—particularly when identifying differentially expressed genes (DEGs). However, it remains unclear whether these are still valid or appropriate data. Therefore, here, we sought characterize performance methods by constructing an in silico simulator with controllable known DEG ground truth. Surprisingly, our findings reveal little variation classic algorithms—all which fail accurately recapture DEGs significant levels. We further demonstrate that cellular heterogeneity within spots is primary cause this poor propose simple gene-selection scheme, based on prior knowledge cell-type specificity, overcome this. Notably, approach outperforms existing data-driven specifically offers improved recovery reliability rates. In summary, work details conceptual framework can be used upstream, agnostically, any algorithm improve accuracy analysis downstream findings.

Язык: Английский

CellPhoneDB v5: inferring cell–cell communication from single-cell multiomics data DOI
Kevin Troulé, Robert Petryszak, Batuhan Çakır

и другие.

Nature Protocols, Год журнала: 2025, Номер unknown

Опубликована: Март 25, 2025

Язык: Английский

Процитировано

1
CELLama: Foundation Model for Single Cell and Spatial Transcriptomics by Cell Embedding Leveraging Language Model Abilities DOI Open Access
Hongyoon Choi, Jeongbin Park, S. K. Kim

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Май 10, 2024

Abstract Large-scale single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) have transformed biomedical research into a data-driven field, enabling the creation of comprehensive data atlases. These methodologies facilitate detailed understanding biology pathophysiology, aiding in discovery new therapeutic targets. However, complexity sheer volume from these technologies present analytical challenges, particularly robust cell typing, integration complex relationships cells. To address we developed CELLama (Cell Embedding Leverage Language Model Abilities), framework that leverage language model to transform ’sentences’ encapsulate gene expressions metadata, universal cellular embedding for various analysis. CELLama, serving as foundation model, supports flexible applications ranging typing analysis contexts, independently manual reference selection or intricate dataset-specific workflows. Our results demonstrate has significant potential determining types across multi-tissue atlases their interactions unraveling tissue dynamics.

Язык: Английский

Процитировано

5
Towards the Next Generation of Data‐Driven Therapeutics Using Spatially Resolved Single‐Cell Technologies and Generative AI DOI Creative Commons

Avital Rodov,

Hosna Baniadam,

Robert Zeiser

и другие.

European Journal of Immunology, Год журнала: 2025, Номер 55(2)

Опубликована: Фев. 1, 2025

ABSTRACT Recent advances in multi‐omics and spatially resolved single‐cell technologies have revolutionised our ability to profile millions of cellular states, offering unprecedented opportunities understand the complex molecular landscapes human tissues both health disease. These developments hold immense potential for precision medicine, particularly rational design novel therapeutics treating inflammatory autoimmune diseases. However, vast, high‐dimensional data generated by these present significant analytical challenges, such as distinguishing technical variation from biological or defining relevant questions that leverage added spatial dimension improve understanding tissue organisation. Generative artificial intelligence (AI), specifically variational autoencoder‐ transformer‐based latent variable models, provides a powerful flexible approach addressing challenges. models make inferences about cell's intrinsic state effectively identifying patterns, reducing dimensionality modelling variability datasets. This review explores current landscape technologies, application generative AI analysis their transformative impact on By combining with advanced methodologies, we highlight insights into pathogenesis disorders outline future directions leveraging achieve goal AI‐powered personalised medicine.

Язык: Английский

Процитировано

0
Precise gene expression deconvolution in spatial transcriptomics with STged DOI Creative Commons
Jia-Juan Tu, Hong Yan, Xiao-Fei Zhang

и другие.

Nucleic Acids Research, Год журнала: 2025, Номер 53(4)

Опубликована: Фев. 2, 2025

Abstract Spatially resolved transcriptomics (SRT) has transformed tissue biology by linking gene expression profiles with spatial information. However, sequencing-based SRT methods aggregate signals from multiple cell types within capture locations (“spots”), masking cell-type-specific patterns. Traditional cell-type deconvolution estimate compositions spots but fail to resolve expression, limiting their ability uncover critical biological processes such as cellular interactions and microenvironmental dynamics. Here, we present STged (spatial transcriptomic deconvolution), a novel computational framework that goes beyond traditional reconstructing mixed spots. integrates graph-based correlations reference-derived signatures using non-negative least-squares regression framework, achieving precise biologically meaningful deconvolution. Comprehensive simulations show consistently outperforms existing in accuracy robustness. Applications human pancreatic ductal adenocarcinoma squamous carcinoma datasets reveal its capacity identify microenvironment-specific highly variable genes, reconstruct cell–cell communication networks, architecture at near-single-cell resolution. In mouse kidney tissues, uncovers dynamic patterns distinct programs, advancing our understanding of heterogeneity

Язык: Английский

Процитировано

0
Unraveling cell–cell communication with NicheNet by inferring active ligands from transcriptomics data DOI
Chananchida Sang-aram, Robin Browaeys, Ruth Seurinck

и другие.

Nature Protocols, Год журнала: 2025, Номер unknown

Опубликована: Март 4, 2025

Язык: Английский

Процитировано

0
Applications of spatial transcriptomics in studying spermatogenesis DOI Creative Commons

Qianlan Xu,

Haiqi Chen

Andrology, Год журнала: 2025, Номер unknown

Опубликована: Апрель 9, 2025

Abstract Spermatogenesis is a complex differentiation process that facilitated by series of cellular and molecular events. High‐throughput genomics approaches, such as single‐cell RNA sequencing, have begun to enable the systematic characterization these However, loss tissue context because disassociations in isolation protocols limits our ability understand regulation spermatogenesis how defects lead infertility. The recent advancement spatial transcriptomics technologies enables studying signatures various cell types their interactions native context. In this review, we discuss has been leveraged identify spatially variable genes, characterize neighborhood, delineate cell‒cell communications, detect changes under pathological conditions mammalian testis. We believe transcriptomics, along with other emerging resolved omics assays, can be utilized further understanding underlying causes male infertility, facilitate development new treatment approaches.

Язык: Английский

Процитировано

0
Unveiling contact-mediated cellular crosstalk DOI
Hyobin Kim, Kwang-eun Kim, Esha Madan

и другие.

Trends in Genetics, Год журнала: 2024, Номер 40(10), С. 868 - 879

Опубликована: Июнь 21, 2024

Язык: Английский

Процитировано

2
Increased spatial coupling of integrin and collagen IV in the immunoresistant clear-cell renal-cell carcinoma tumor microenvironment DOI Creative Commons
Alex C. Soupir, Mitchell Hayes, Taylor Peak

и другие.

Genome biology, Год журнала: 2024, Номер 25(1)

Опубликована: Дек. 5, 2024

Abstract Background Immunotherapy has improved survival for patients with advanced clear cell renal carcinoma (ccRCC), but resistance to therapy develops in most patients. We use cellular-resolution spatial transcriptomics immunotherapy naïve and exposed primary ccRCC tumors better understand resistance. Results Spatial molecular imaging of tumor adjacent stroma samples from 21 suggests that viable following harbor more stromal CD8 + T cells neutrophils than tumors. YES1 is significantly upregulated cells. GSEA shows the epithelial-mesenchymal transition pathway spatially enriched associated ligand-receptor transcript pair COL4A1 - ITGAV higher autocorrelation after exposure immunotherapy. More integrin αV are observed on multiplex immunofluorescence validation. Compared other cancers TCGA, have highest expression both . Assessing bulk RNA proteomic correlates CPTAC databases reveals collagen IV protein abundant stages disease. Conclusions 3 patient cohorts cRCC indicates autocorrelated immunotherapy-exposed compared immunotherapy-naïve tumors, high among fibroblasts, cells, endothelium. Further research needed changes immune microenvironment explore potential therapeutic role treatment.

Язык: Английский

Процитировано

2
Rapid and memory-efficient analysis and quality control of large spatial transcriptomics datasets DOI Creative Commons
Bence Kӧvér, Alessandra Vigilante

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Июль 24, 2024

Abstract The 10x Visium spatial transcriptomics platform has been widely adopted due to its established analysis pipelines, robust community support, and manageable data output. However, technologies like have the limitation of being low-resolution, recently platforms with subcellular resolution proliferated. Such high-resolution datasets pose significant computational challenges for analysis, regards memory requirement processing speed. Here, we introduce Pseudovisium, a Python-based framework designed facilitate rapid memory-efficient quality control interoperability data. This is achieved by mimicking structure through hexagonal binning transcripts. Analysis 47 publicly available concluded that Pseudovisium increased speed reduced dataset size more than an order magnitude. At same time, it preserved key biological signatures, such as spatially variable genes, enriched gene sets, cell populations, gene-gene correlations. allows accurate simulation experiments, facilitating comparisons between guiding experimental design. Specifically, found high concordance (derived from Xenium or CosMx) consecutive tissue slices. We further demonstrate Pseudovisium’s utility performing on large-scale Xenium, CosMx, MERSCOPE platforms, identifying similar replicates, well potentially low-quality samples probes. common format provided also enabled direct comparison metrics across 6 59 datasets, revealing differences in transcript capture efficiency quality. Lastly, merging joint demonstrated identification shared clusters sets mouse brain using multiple platforms. By lowering requirements enhancing reusability data, democratizes wet-lab scientists enables novel insights.

Язык: Английский

Процитировано

1
Spatial Transcriptomics Identifies Cellular and Molecular Characteristics of Scleroderma Skin Lesions: Pilot Study in Juvenile Scleroderma DOI Open Access
Tianhao Liu, Deren Esencan, Cláudia Salgado

и другие.

International Journal of Molecular Sciences, Год журнала: 2024, Номер 25(17), С. 9182 - 9182

Опубликована: Авг. 23, 2024

Juvenile localized and systemic scleroderma are rare autoimmune diseases which cause significant disability morbidity in children. The mechanisms driving juvenile remain unclear, necessitating further cellular molecular level studies. Visium CytAssist spatial transcriptomics (ST) platform, preserves the location of cells simultaneously sequences whole transcriptome, was employed to profile histopathological slides from skin lesions patients. (1) Spatial domains were identified ST data exhibited strong concordance with pathologist's annotations anatomical structures. (2) integration paired single-cell RNA sequencing (scRNA-seq) same patients validated comparable accuracy two platforms facilitated estimation cell type composition data. (3) pathologist-annotated immune infiltrates, such as perivascular clearly delineated by analysis, underscoring biological relevance findings. This is first study utilizing investigate validity corroborated gene expression analyses assessments. Integration scRNA-seq type-level analysis validation. Analyses infiltrates through combined pathological review enhances our understanding pathogenesis scleroderma.

Язык: Английский

Процитировано

1