細胞の家系をたどる新しいツール(A new tool for tracing the family trees of cells)

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2024-04-11 スイス連邦工科大学ローザンヌ校(EPFL)

生物学者たちは、生物のさまざまな部分を形成するために細胞が増殖し分化する過程で、各細胞の起源を理解することが重要です。これが「細胞系譜」と呼ばれるもので、細胞の家系図のようなものです。スイスのEPFLの研究チームは、Gene Expression Memory-based Lineage Inference(GEMLI)という計算ツールを開発しました。このツールは、単一細胞RNAシーケンスデータを使用し、遺伝子発現パターンを解析して細胞の系譜関係を再構築します。GEMLIは、広範囲の細胞タイプと状態に対応し、細胞の系譜を再現することができます。

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scRNA-seqデータセットからの遺伝子発現記憶に基づく細胞系列の予測 Gene-expression memory-based prediction of cell lineages from scRNA-seq datasets

A. S. Eisele,M. Tarbier,A. A. Dormann,V. Pelechano & D. M. Suter
Nature Communications  Published:29 March 2024
DOI:https://doi.org/10.1038/s41467-024-47158-y

細胞の家系をたどる新しいツール(A new tool for tracing the family trees of cells)

Abstract

Assigning single cell transcriptomes to cellular lineage trees by lineage tracing has transformed our understanding of differentiation during development, regeneration, and disease. However, lineage tracing is technically demanding, often restricted in time-resolution, and most scRNA-seq datasets are devoid of lineage information. Here we introduce Gene Expression Memory-based Lineage Inference (GEMLI), a computational tool allowing to robustly identify small to medium-sized cell lineages solely from scRNA-seq datasets. GEMLI allows to study heritable gene expression, to discriminate symmetric and asymmetric cell fate decisions and to reconstruct individual multicellular structures from pooled scRNA-seq datasets. In human breast cancer biopsies, GEMLI reveals previously unknown gene expression changes at the onset of cancer invasiveness. The universal applicability of GEMLI allows studying the role of small cell lineages in a wide range of physiological and pathological contexts, notably in vivo. GEMLI is available as an R package on GitHub (https://github.com/UPSUTER/GEMLI).

生物工学一般
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