Targeting nucleotide metabolic pathways in colorectal cancer by integrating scRNA-seq, spatial transcriptome, and bulk RNA-seq data DOI Creative Commons
Songyun Zhao, Pengpeng Zhang, Sen Niu

et al.

Functional & Integrative Genomics, Journal Year: 2024, Volume and Issue: 24(2)

Published: April 1, 2024

Colorectal cancer is a malignant tumor of the digestive system originating from abnormal cell proliferation in colon or rectum, often leading to gastrointestinal symptoms and severe health issues. Nucleotide metabolism, which encompasses synthesis DNA RNA, pivotal cellular biochemical process that significantly impacts both progression therapeutic strategies colorectal METHODS: For single-cell RNA sequencing (scRNA-seq), five functions were employed calculate scores related nucleotide metabolism. Cell developmental trajectory analysis intercellular interaction utilized explore metabolic characteristics communication patterns different epithelial cells. These findings further validated using spatial transcriptome (stRNA-seq). A risk model was constructed expression profile data TCGA GEO cohorts optimize clinical decision-making. Key metabolism-related genes (NMRGs) functionally by vitro experiments.

Language: Английский

Integrative multi-omics approaches to explore immune cell functions: Challenges and opportunities DOI Creative Commons
Xu Wang,

Fan Dian,

Yuqing Yang

et al.

iScience, Journal Year: 2023, Volume and Issue: 26(4), P. 106359 - 106359

Published: March 9, 2023

As modern biological sciences evolve from investigation of individual molecules and pathways to growing emphasis on global systems-based processes, increasing efforts have focused combining the study genomics with that other omics technologies, including epigenomics, transcriptomics, quantitative proteomics, analyses post-translational modifications (PTMs) metabolomics, characterize specific or pathological processes. In addition, emerging genome-wide functional screening technologies further help researchers identify key regulators immune functions. Derived these multi-omics single cell sequencing analysis multiple layers offers an overview intra-tissue intra-organ heterogeneity. this review, we summarize advances in tools explore functions applications approaches clinical disorders, aiming provide outlook potential opportunities challenges pose future field immunology.

Language: Английский

Citations

25
Biomarker discovery in hepatocellular carcinoma (HCC) for personalized treatment and enhanced prognosis DOI

Baofa Yu,

Wenxue Ma

Cytokine & Growth Factor Reviews, Journal Year: 2024, Volume and Issue: 79, P. 29 - 38

Published: Aug. 24, 2024

Language: Английский

Citations

12
STEM enables mapping of single-cell and spatial transcriptomics data with transfer learning DOI Creative Commons
Minsheng Hao, Erpai Luo, Yixin Chen

et al.

Communications Biology, Journal Year: 2024, Volume and Issue: 7(1)

Published: Jan. 6, 2024

Abstract Profiling spatial variations of cellular composition and transcriptomic characteristics is important for understanding the physiology pathology tissues. Spatial transcriptomics (ST) data depict gene expression but currently dominating high-throughput technology yet not at single-cell resolution. Single-cell RNA-sequencing (SC) provide information level lack information. Integrating these two types would be ideal revealing landscapes We develop method STEM (SpaTially aware EMbedding) this purpose. It uses deep transfer learning to encode both ST SC into a unified spatially embedding space, then embeddings infer SC-ST mapping predict pseudo-spatial adjacency between cells in data. Semi-simulation real experiments verify that preserved eliminated technical biases apply human squamous cell carcinoma hepatic lobule datasets uncover localization rare reveal cell-type-specific variation along axis. powerful build landscapes, can mechanistic insights heterogeneity microenvironments

Language: Английский

Citations

10
Applications of single‑cell omics and spatial transcriptomics technologies in gastric cancer (Review) DOI Open Access
Liping Ren,

Danni Huang,

Hongjiang Liu

et al.

Oncology Letters, Journal Year: 2024, Volume and Issue: 27(4)

Published: Feb. 14, 2024

Gastric cancer (GC) is a prominent contributor to global cancer‑related mortalities, and deeper understanding of its molecular characteristics tumor heterogeneity required. Single‑cell omics spatial transcriptomics (ST) technologies have revolutionized research by enabling the exploration cellular landscapes at single‑cell level. In present review, an overview advancements in ST their applications GC provided. Firstly, multiple methods are discussed, highlighting ability offer unique insights into gene expression, genetic alterations, epigenomic modifications, protein expression patterns location tissues. Furthermore, summary provided key findings from previous on used GC, which valuable diagnosis prognosis, microenvironment analysis, treatment response. summary, application has revealed levels holds promise for improving diagnostics, personalized treatments patient outcomes GC.

Language: Английский

Citations

9
Macrophage diversity in human cancers: New insight provided by single-cell resolution and spatial context DOI Creative Commons
Militsa Rakina, Irina Larionova, Julia Kzhyshkowska

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(7), P. e28332 - e28332

Published: March 22, 2024

M1/M2 paradigm of macrophage plasticity has existed for decades. Now it becomes clear that this dichotomy doesn't adequately reflect the diversity phenotypes in tumor microenvironment (TME). Tumor-associated macrophages (TAMs) are a major population innate immune cells TME promotes cell proliferation, angiogenesis and lymphangiogenesis, invasion metastatic niche formation, as well response to anti-tumor therapy. However, fundamental restriction therapeutic TAM targeting is limited knowledge about specific states distinct human cancer types. Here we summarized results most recent studies use advanced technologies (e.g. single-cell RNA sequencing spatial transcriptomics) allowing decipher novel functional subsets TAMs numerous cancers. The transcriptomic profiles these their clinical significance were described. We emphasized characteristics subpopulations – TREM2+, SPP1+, MARCO+, FOLR2+, SIGLEC1+, APOC1+, C1QC+, others, which have been extensively characterized several cancers, associated with prognosis. Spatial transcriptomics defined interactions between other types, especially fibroblasts, tumors. methods also applied identify markers immunotherapy response, expressed by or macrophage-abundant regions. highlighted perspectives techniques utilize single resolution investigating new ligand-receptor effective based on TAM-targeting.

Language: Английский

Citations

9
Accurate single-molecule spot detection for image-based spatial transcriptomics with weakly supervised deep learning DOI Creative Commons
Emily Laubscher, Xuefei Wang, Nitzan Razin

et al.

Cell Systems, Journal Year: 2024, Volume and Issue: 15(5), P. 475 - 482.e6

Published: May 1, 2024

Image-based spatial transcriptomics methods enable transcriptome-scale gene expression measurements with information but require complex, manually tuned analysis pipelines. We present Polaris, an pipeline for image-based that combines deep-learning models cell segmentation and spot detection a probabilistic decoder to quantify single-cell accurately. Polaris offers unifying, turnkey solution analyzing data from multiplexed error-robust FISH (MERFISH), sequential fluorescence in situ hybridization (seqFISH), or RNA sequencing (ISS) experiments. is available through the DeepCell software library (https://github.com/vanvalenlab/deepcell-spots) https://www.deepcell.org.

Language: Английский

Citations

9
Improving Spatial Transcriptomics with Membrane‐Based Boundary Definition and Enhanced Single‐Cell Resolution DOI Open Access
Song Li, Liqun Wang,

Zitian He

et al.

Small Methods, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 28, 2025

Abstract Accurately defining cell boundaries for spatial transcriptomics is technically challenging. The current major approaches are nuclear staining or mathematical inference, which either exclude the cytoplasm determine a hypothetical boundary. Here, new method introduced boundaries: labeling membranes using genetically coded fluorescent proteins, allows precise indexing of sequencing spots and transcripts within cells on sections. Use this membrane‐based greatly increases number genes captured in compared to nucleus‐based methods; numbers increased by 67% 119% mouse axolotl livers, respectively. obtained expression profiles more consistent with single‐cell RNA‐seq data, demonstrating rational clustering apparent type‐specific markers. Furthermore, improved resolution achieved better identify rare types elaborate domains brain intestine. In addition regular cells, accurate recognition multinucleated lacking nuclei liver achieved, its ability analyze complex tissues organs, not achievable previous methods. This study provides powerful tool improving that has broad potential applications biological medical sciences.

Language: Английский

Citations

1
Biomimetic In Vitro Lung Models: Current Challenges and Future Perspective DOI Creative Commons
Ali Doryab, Jürgen Gröll

Advanced Materials, Journal Year: 2023, Volume and Issue: 35(13)

Published: Feb. 7, 2023

As post-COVID complications, chronic respiratory diseases are one of the foremost causes mortality. The quest for a cure this recent global challenge underlines that lack predictive in vitro lung models is main bottlenecks pulmonary preclinical drug development. Despite rigorous efforts to develop biomimetic models, current cutting-edge represent compromise numerous technological and biological aspects. Most advanced still "proof-of-concept" phase with low clinical translation findings. On other hand, advances cellular molecular studies mainly based on relatively simple unrealistic models. Herein, challenges potential strategies toward not only bioinspired but truly discussed.

Language: Английский

Citations

19
Autophagy in Spinocerebellar Ataxia Type 3: From Pathogenesis to Therapeutics DOI Open Access
Rodrigo Paulino, Clévio Nóbrega

International Journal of Molecular Sciences, Journal Year: 2023, Volume and Issue: 24(8), P. 7405 - 7405

Published: April 17, 2023

Machado–Joseph disease (MJD) or spinocerebellar ataxia 3 (SCA3) is a rare, inherited, monogenic, neurodegenerative disease, and the most common SCA worldwide. MJD/SCA3 causative mutation an abnormal expansion of triplet CAG at exon 10 within ATXN3 gene. The gene encodes for ataxin-3, which deubiquitinating protein that also involved in transcriptional regulation. In normal conditions, ataxin-3 polyglutamine stretch has between 13 49 glutamines. However, patients, size increases from 55 to 87, contributing conformation, insolubility, aggregation. formation aggregates, hallmark MJD/SCA3, compromises different cell pathways, leading impairment clearance mechanisms, such as autophagy. patients display several signals symptoms prominent ataxia. Neuropathologically, regions affected are cerebellum pons. Currently, there no disease-modifying therapies, rely only on supportive symptomatic treatments. Due these facts, huge research effort develop therapeutic strategies this incurable disease. This review aims bring together current state-of-the-art regarding autophagy pathway focusing evidence its context and, importantly, targeting development pharmacological gene-based therapies.

Language: Английский

Citations

19
Omics Technologies Improving Breast Cancer Research and Diagnostics DOI Open Access

Arianna Orsini,

Chiara Diquigiovanni, Elena Bonora

et al.

International Journal of Molecular Sciences, Journal Year: 2023, Volume and Issue: 24(16), P. 12690 - 12690

Published: Aug. 11, 2023

Breast cancer (BC) has yielded approximately 2.26 million new cases and caused nearly 685,000 deaths worldwide in the last two years, making it most common diagnosed type world. BC is an intricate ecosystem formed by both tumor microenvironment malignant cells, its heterogeneity impacts response to treatment. Biomedical research entered era of massive omics data thanks high-throughput sequencing revolution, quick progress widespread adoption. These technologies—liquid biopsy, transcriptomics, epigenomics, proteomics, metabolomics, pharmaco-omics artificial intelligence imaging—could help researchers clinicians better understand formation evolution BC. This review focuses on findings recent multi-omics-based that been applied research, with introduction every technique their applications for different phenotypes, biomarkers, target therapies, diagnosis, treatment prognosis, provide a comprehensive overview possibilities research.

Language: Английский

Citations

19