A cellular hierarchy framework for understanding heterogeneity and predicting drug response in acute myeloid leukemia

Nature Medicine, Год журнала: 2022, Номер 28(6), С. 1212 - 1223

Опубликована: Май 26, 2022

Язык: Английский

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Automated methods for cell type annotation on scRNA-seq data

Computational and Structural Biotechnology Journal, Год журнала: 2021, Номер 19, С. 961 - 969

Опубликована: Янв. 1, 2021

The advent of single-cell sequencing started a new era transcriptomic and genomic research, advancing our knowledge the cellular heterogeneity dynamics. Cell type annotation is crucial step in analyzing RNA data, yet manual time-consuming partially subjective. As an alternative, tools have been developed for automatic cell identification. Different strategies emerged to ultimately associate gene expression profiles single cells with either by using curated marker databases, correlating reference or transferring labels supervised classification. In this review, we present overview available underlying approaches perform automated annotations on scRNA-seq data.

Язык: Английский

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Dental cell type atlas reveals stem and differentiated cell types in mouse and human teeth

Nature Communications, Год журнала: 2020, Номер 11(1)

Опубликована: Сен. 23, 2020

Abstract Understanding cell types and mechanisms of dental growth is essential for reconstruction engineering teeth. Therefore, we investigated cellular composition growing non-growing mouse human As a result, report an unappreciated complexity the continuously-growing incisor, which suggests coherent model dynamics enabling unarrested growth. This relies on spatially-restricted stem, progenitor differentiated populations in epithelial mesenchymal compartments underlying coordinated expansion two major branches pulpal cells diverse subtypes. Further comparisons teeth yield both parallelisms differences tissue heterogeneity highlight specifics behind modes. Despite being similar at coarse level, reveal molecular species-specific subtypes suggesting possible evolutionary divergence. Overall, here provide atlas with focus differentiation.

Язык: Английский

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The specious art of single-cell genomics

PLoS Computational Biology, Год журнала: 2023, Номер 19(8), С. e1011288 - e1011288

Опубликована: Авг. 17, 2023

Dimensionality reduction is standard practice for filtering noise and identifying relevant features in large-scale data analyses. In biology, single-cell genomics studies typically begin with to 2 or 3 dimensions produce "all-in-one" visuals of the that are amenable human eye, these subsequently used qualitative quantitative exploratory analysis. However, there little theoretical support this practice, we show extreme dimension reduction, from hundreds thousands 2, inevitably induces significant distortion high-dimensional datasets. We therefore examine practical implications low-dimensional embedding find extensive distortions inconsistent practices make such embeddings counter-productive exploratory, biological lieu this, discuss alternative approaches conducting targeted feature exploration enable hypothesis-driven discovery.

Язык: Английский

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Efficient and precise single-cell reference atlas mapping with Symphony

Nature Communications, Год журнала: 2021, Номер 12(1)

Опубликована: Окт. 7, 2021

Abstract Recent advances in single-cell technologies and integration algorithms make it possible to construct comprehensive reference atlases encompassing many donors, studies, disease states, sequencing platforms. Much like mapping reads a genome, is essential be able map query cells onto complex, multimillion-cell rapidly identify relevant cell states phenotypes. We present Symphony ( https://github.com/immunogenomics/symphony ), an algorithm for building large-scale, integrated convenient, portable format that enables efficient within seconds. localizes stable low-dimensional embedding, facilitating reproducible downstream transfer of reference-defined annotations the query. demonstrate power multiple real-world datasets, including (1) multi-donor, multi-species predict pancreatic types, (2) localizing along developmental trajectory fetal liver hematopoiesis, (3) inferring surface protein expression with multimodal CITE-seq atlas memory T cells.

Язык: Английский

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Pathogenic variants damage cell composition and single cell transcription in cardiomyopathies

Science, Год журнала: 2022, Номер 377(6606)

Опубликована: Авг. 4, 2022

Pathogenic variants in genes that cause dilated cardiomyopathy (DCM) and arrhythmogenic (ACM) convey high risks for the development of heart failure through unknown mechanisms. Using single-nucleus RNA sequencing, we characterized transcriptome 880,000 nuclei from 18 control 61 failing, nonischemic human hearts with pathogenic DCM ACM or idiopathic disease. We performed genotype-stratified analyses ventricular cell lineages transcriptional states. The resultant atlas demonstrated distinct right left responses, highlighting genotype-associated pathways, intercellular interactions, differential gene expression at single-cell resolution. Together, these data illuminate both shared cellular molecular architectures suggest candidate therapeutic targets.

Язык: Английский

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DestVI identifies continuums of cell types in spatial transcriptomics data

Nature Biotechnology, Год журнала: 2022, Номер 40(9), С. 1360 - 1369

Опубликована: Апрель 21, 2022

Язык: Английский

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The Specious Art of Single-Cell Genomics

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2021, Номер unknown

Опубликована: Авг. 26, 2021

Abstract Dimensionality reduction is standard practice for filtering noise and identifying relevant features in large-scale data analyses. In biology, single-cell genomics studies typically begin with to two or three dimensions produce ‘all-in-one’ visuals of the that are amenable human eye, these subsequently used qualitative quantitative exploratory analysis. However, there little theoretical support this practice, we show extreme dimension reduction, from hundreds thousands two, inevitably induces significant distortion high-dimensional datasets. We therefore examine practical implications low-dimensional embedding data, find extensive distortions inconsistent practices make such embeddings counter-productive exploratory, biological lieu this, discuss alternative approaches conducting targeted feature exploration, enable hypothesis-driven discovery.

Язык: Английский

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Dictionary learning for integrative, multimodal, and scalable single-cell analysis

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2022, Номер unknown

Опубликована: Фев. 26, 2022

Abstract Mapping single-cell sequencing profiles to comprehensive reference datasets represents a powerful alternative unsupervised analysis. Reference datasets, however, are predominantly constructed from RNA-seq data, and cannot be used annotate that do not measure gene expression. Here we introduce ‘bridge integration’, method harmonize singlecell across modalities by leveraging multi-omic dataset as molecular bridge. Each cell in the comprises an element ‘dictionary’, which can reconstruct unimodal transform them into shared space. We demonstrate our procedure accurately transcriptomic data with independent single measurements of chromatin accessibility, histone modifications, DNA methylation, protein levels. Moreover, how dictionary learning combined sketching techniques substantially improve computational scalability, 8.6 million human immune mass cytometry experiments. Our approach aims broaden utility facilitate comparisons diverse modalities. Availability Installation instructions, documentations, vignettes available at http://www.satijalab.org/seurat

Язык: Английский

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Large-scale foundation model on single-cell transcriptomics

Nature Methods, Год журнала: 2024, Номер 21(8), С. 1481 - 1491

Опубликована: Июнь 6, 2024

Язык: Английский

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