Single‐cell RNA sequencing technologies and applications: A brief overview

Clinical and Translational Medicine, Journal Year: 2022, Volume and Issue: 12(3)

Published: March 1, 2022

Language: Английский

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Applications of single-cell RNA sequencing in drug discovery and development

Nature Reviews Drug Discovery, Journal Year: 2023, Volume and Issue: 22(6), P. 496 - 520

Published: April 28, 2023

Single-cell technologies, particularly single-cell RNA sequencing (scRNA-seq) methods, together with associated computational tools and the growing availability of public data resources, are transforming drug discovery development. New opportunities emerging in target identification owing to improved disease understanding through cell subtyping, highly multiplexed functional genomics screens incorporating scRNA-seq enhancing credentialling prioritization. ScRNA-seq is also aiding selection relevant preclinical models providing new insights into mechanisms action. In clinical development, can inform decision-making via biomarker for patient stratification more precise monitoring response progression. Here, we illustrate how methods being applied key steps discuss ongoing challenges their implementation pharmaceutical industry. There have been significant recent advances development remarkable Ferran colleagues primarily pipeline, from decision-making. Ongoing potential future directions discussed.

Language: Английский

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Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2024

Nucleic Acids Research, Journal Year: 2023, Volume and Issue: 52(D1), P. D18 - D32

Published: Nov. 29, 2023

Abstract The National Genomics Data Center (NGDC), which is a part of the China for Bioinformation (CNCB), provides family database resources to support global academic and industrial communities. With rapid accumulation multi-omics data at an unprecedented pace, CNCB-NGDC continuously expands updates core through big archiving, integrative analysis value-added curation. Importantly, NGDC collaborates closely with major international databases initiatives ensure seamless exchange interoperability. Over past year, significant efforts have been dedicated integrating diverse omics data, synthesizing expanding knowledge, developing new resources, upgrading existing resources. Particularly, several are newly developed biodiversity protists (P10K), bacteria (NTM-DB, MPA) as well plant (PPGR, SoyOmics, PlantPan) disease/trait association (CROST, HervD Atlas, HALL, MACdb, BioKA, RePoS, PGG.SV, NAFLDkb). All services publicly accessible https://ngdc.cncb.ac.cn.

Language: Английский

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Large-scale foundation model on single-cell transcriptomics

Nature Methods, Journal Year: 2024, Volume and Issue: 21(8), P. 1481 - 1491

Published: June 6, 2024

Language: Английский

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The Evolution of Single-Cell RNA Sequencing Technology and Application: Progress and Perspectives

International Journal of Molecular Sciences, Journal Year: 2023, Volume and Issue: 24(3), P. 2943 - 2943

Published: Feb. 2, 2023

As an emerging sequencing technology, single-cell RNA (scRNA-Seq) has become a powerful tool for describing cell subpopulation classification and heterogeneity by achieving high-throughput multidimensional analysis of individual cells circumventing the shortcomings traditional detecting average transcript level populations. It been applied to life science medicine research fields such as tracking dynamic differentiation, revealing sensitive effector cells, key molecular events diseases. This review focuses on recent technological innovations in scRNA-Seq, highlighting latest results with scRNA-Seq core technology frontier areas embryology, histology, oncology, immunology. In addition, this outlines prospects its innovative application Chinese (TCM) discusses issues currently being addressed great potential exploring disease diagnostic targets uncovering drug therapeutic combination multiomics technologies.

Language: Английский

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Large Scale Foundation Model on Single-cell Transcriptomics

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: May 31, 2023

Abstract Large-scale pretrained models have become foundation leading to breakthroughs in natural language processing and related fields. Developing life science for deciphering the “languages” of cells facilitating biomedical research is promising yet challenging. We developed a large-scale model scFoundation with 100M parameters this purpose. was trained on over 50 million human single-cell transcriptomics data, which contain high-throughput observations complex molecular features all known types cells. currently largest terms size trainable parameters, dimensionality genes number used pre-training. Experiments showed that can serve as achieve state-of-the-art performances diverse array downstream tasks, such gene expression enhancement, tissue drug response prediction, classification, perturbation prediction.

Language: Английский

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Batch alignment of single-cell transcriptomics data using deep metric learning

Nature Communications, Journal Year: 2023, Volume and Issue: 14(1)

Published: Feb. 21, 2023

scRNA-seq has uncovered previously unappreciated levels of heterogeneity. With the increasing scale studies, major challenge is correcting batch effect and accurately detecting number cell types, which inevitable in human studies. The majority algorithms have been specifically designed to remove firstly then conduct clustering, may miss some rare types. Here we develop scDML, a deep metric learning model data, guided by initial clusters nearest neighbor information intra inter batches. Comprehensive evaluations spanning different species tissues demonstrated that scDML can effect, improve clustering performance, recover true types consistently outperform popular methods such as Seurat 3, scVI, Scanorama, BBKNN, Harmony et al. Most importantly, preserves subtle raw data enables discovery new subtypes are hard extract analyzing each individually. We also show scalable large datasets with lower peak memory usage, believe offers valuable tool study complex cellular

Language: Английский

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The role of tumor microenvironment in drug resistance: emerging technologies to unravel breast cancer heterogeneity

Frontiers in Oncology, Journal Year: 2023, Volume and Issue: 13

Published: May 17, 2023

Breast cancer is a highly heterogeneous disease, at both inter- and intra-tumor levels, this heterogeneity crucial determinant of malignant progression response to treatments. In addition genetic diversity plasticity cells, the tumor microenvironment contributes shaping physical biological surroundings tumor. The activity certain types immune, endothelial or mesenchymal cells in can change effectiveness therapies via plethora different mechanisms. Therefore, deciphering interactions between distinct cell types, their spatial organization specific contribution growth drug sensitivity still major challenge. Dissecting currently an urgent need better define breast biology develop therapeutic strategies targeting as helpful tools for combined personalized treatment. review, we analyze mechanisms by which affects characteristics that ultimately result resistance, outline state art preclinical models emerging technologies will be instrumental unraveling impact on resistance therapies.

Language: Английский

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Effective multi-modal clustering method via skip aggregation network for parallel scRNA-seq and scATAC-seq data

Briefings in Bioinformatics, Journal Year: 2024, Volume and Issue: 25(2)

Published: Jan. 22, 2024

Abstract In recent years, there has been a growing trend in the realm of parallel clustering analysis for single-cell RNA-seq (scRNA) and Assay Transposase Accessible Chromatin (scATAC) data. However, prevailing methods often treat these two data modalities as equals, neglecting fact that scRNA mode holds significantly richer information compared to scATAC. This disregard hinders model benefits from insights derived multiple modalities, compromising overall performance. To this end, we propose an effective multi-modal scEMC Concretely, have devised skip aggregation network simultaneously learn global structural among cells integrate diverse modalities. safeguard quality integrated cell representation against influence stemming sparse scATAC data, connect with aggregated via connection. Moreover, effectively fit real distribution cells, introduced Zero Inflated Negative Binomial-based denoising autoencoder accommodates corrupted containing synthetic noise, concurrently integrating joint optimization module employs losses. Extensive experiments serve underscore effectiveness our model. work contributes ongoing exploration subpopulations tumor microenvironments, code will be public at https://github.com/DayuHuu/scEMC.

Language: Английский

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Single-cell sequencing to multi-omics: technologies and applications

Biomarker Research, Journal Year: 2024, Volume and Issue: 12(1)

Published: Sept. 27, 2024

Abstract Cells, as the fundamental units of life, contain multidimensional spatiotemporal information. Single-cell RNA sequencing (scRNA-seq) is revolutionizing biomedical science by analyzing cellular state and intercellular heterogeneity. Undoubtedly, single-cell transcriptomics has emerged one most vibrant research fields today. With optimization innovation technologies, intricate details concealed within cells are gradually unveiled. The combination scRNA-seq other multi-omics at forefront field. This involves simultaneously measuring various omics data individual cells, expanding our understanding across a broader spectrum dimensions. precisely captures aspects transcriptomes, immune repertoire, spatial information, temporal epitopes, in diverse contexts. In addition to depicting cell atlas normal or diseased tissues, it also provides cornerstone for studying differentiation development patterns, disease heterogeneity, drug resistance mechanisms, treatment strategies. Herein, we review traditional technologies outline latest advancements multi-omics. We summarize current status challenges applying biological clinical applications. Finally, discuss limitations potential strategies address them.

Language: Английский

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