Gene regulatory mechanisms guiding bifurcation of inhibitory and excitatory neuron lineages in the anterior brainstem DOI Open Access
Sami Kilpinen, Laura Virtanen,

Silvana Bodington-Celma

et al.

Published: April 10, 2025

Selector transcription factors control choices of alternative cellular fates during development. The ventral rhombomere 1 the embryonic brainstem produces neuronal precursors that can differentiate into either inhibitory GABAergic or excitatory glutamatergic neurons important for behaviour. Transcription (TFs) Tal1 , Gata2 and Gata3 are required adopting identity inhibiting identity. Here, we asked how these selector TFs activated they developing neurons. We addressed questions by analysing chromatin accessibility at putative gene regulatory elements active neuron lineage bifurcation, combined with studies factor expression DNA-binding. Our results show genes highly similar mechanisms, connections to regional patterning, neurogenic cell cycle exit general course differentiation. After activation, linked auto- cross-regulation as well interactions branch. Predicted targets include expressed in neurons, both. Unlike specific branch, appear be under combinatorial . Understanding affecting anterior differentiation may give genetic mechanistic insights neurodevelopmental traits disorders.

Language: Английский

Cellular communities reveal trajectories of brain ageing and Alzheimer’s disease DOI
Gilad Sahar Green, Masashi Fujita, Hyun‐Sik Yang

et al.

Nature, Journal Year: 2024, Volume and Issue: 633(8030), P. 634 - 645

Published: Aug. 28, 2024

Language: Английский

Citations

43
Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics DOI
Gunsagar S. Gulati,

Jeremy Philip D’Silva,

Yunhe Liu

et al.

Nature Reviews Molecular Cell Biology, Journal Year: 2024, Volume and Issue: 26(1), P. 11 - 31

Published: Aug. 21, 2024

Language: Английский

Citations

37
CellRank 2: unified fate mapping in multiview single-cell data DOI Creative Commons
Philipp Weiler, Marius Lange, Michal Klein

et al.

Nature Methods, Journal Year: 2024, Volume and Issue: 21(7), P. 1196 - 1205

Published: June 13, 2024

Abstract Single-cell RNA sequencing allows us to model cellular state dynamics and fate decisions using expression similarity or velocity reconstruct state-change trajectories; however, trajectory inference does not incorporate valuable time point information utilize additional modalities, whereas methods that address these different data views cannot be combined do scale. Here we present CellRank 2, a versatile scalable framework study multiview single-cell of up millions cells in unified fashion. 2 consistently recovers terminal states probabilities across modalities human hematopoiesis endodermal development. Our also combining transitions within experimental points, feature use recover genes promoting medullary thymic epithelial cell formation during pharyngeal endoderm Moreover, enable estimating cell-specific transcription degradation rates from metabolic-labeling data, which apply an intestinal organoid system delineate differentiation trajectories pinpoint regulatory strategies.

Language: Английский

Citations

33
Tumour vasculature at single-cell resolution DOI
Pan Xu, Xin Li, Liang Dong

et al.

Nature, Journal Year: 2024, Volume and Issue: 632(8024), P. 429 - 436

Published: July 10, 2024

Language: Английский

Citations

30
A tunable human intestinal organoid system achieves controlled balance between self-renewal and differentiation DOI Creative Commons
Yang Li,

Xulei Wang,

Xingyu Zhou

et al.

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: Jan. 2, 2025

Language: Английский

Citations

2
Application of Deep Learning on Single-Cell RNA Sequencing Data Analysis: A Review DOI Creative Commons
Matthew Brendel, Chang Su, Zilong Bai

et al.

Genomics Proteomics & Bioinformatics, Journal Year: 2022, Volume and Issue: 20(5), P. 814 - 835

Published: Oct. 1, 2022

Abstract Single-cell RNA sequencing (scRNA-seq) has become a routinely used technique to quantify the gene expression profile of thousands single cells simultaneously. Analysis scRNA-seq data plays an important role in study cell states and phenotypes, helped elucidate biological processes, such as those occurring during development complex organisms, improved our understanding disease states, cancer, diabetes, coronavirus 2019 (COVID-19). Deep learning, recent advance artificial intelligence that been address many problems involving large datasets, also emerged promising tool for analysis, it capacity extract informative compact features from noisy, heterogeneous, high-dimensional improve downstream analysis. The present review aims at surveying recently developed deep learning techniques identifying key steps within analysis pipeline have advanced by explaining benefits over more conventional analytic tools. Finally, we summarize challenges current approaches faced discuss potential directions improvements algorithms

Language: Английский

Citations

46
OmicVerse: a framework for bridging and deepening insights across bulk and single-cell sequencing DOI Creative Commons
Zehua Zeng,

Yuqing Ma,

Lei Hu

et al.

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: July 16, 2024

Abstract Single-cell sequencing is frequently affected by “omission” due to limitations in throughput, yet bulk RNA-seq may contain these ostensibly “omitted” cells. Here, we introduce the single cell trajectory blending from Bulk (BulkTrajBlend) algorithm, a component of OmicVerse suite that leverages Beta-Variational AutoEncoder for data deconvolution and graph neural networks discovery overlapping communities. This approach effectively interpolates restores continuity cells within single-cell RNA datasets. Furthermore, provides an extensive toolkit both analysis, offering seamless access diverse methodologies, streamlining computational processes, fostering exquisite visualization, facilitating extraction significant biological insights advance scientific research.

Language: Английский

Citations

17
Transplanted human intestinal organoids: a resource for modeling human intestinal development DOI Creative Commons
Akaljot Singh, Holly M. Poling, Praneet Chaturvedi

et al.

Development, Journal Year: 2023, Volume and Issue: 150(9)

Published: April 18, 2023

The in vitro differentiation of pluripotent stem cells into human intestinal organoids (HIOs) has served as a powerful means for creating complex three-dimensional structures. Owing to their diverse cell populations, transplantation an animal host is supported with this system and allows the temporal formation fully laminated structures, including crypt-villus architecture smooth muscle layers that resemble native intestine. Although endpoint HIO engraftment been well described, here we aim elucidate developmental stages establish whether it parallels fetal development. We analyzed time course transplanted HIOs histologically at 2, 4, 6 8 weeks post-transplantation, demonstrated maturation closely resembles key also utilized single-nuclear RNA sequencing determine track emergence distinct populations over time, validated our transcriptomic data through situ protein expression. These observations suggest do indeed recapitulate early development, solidifying value model system.

Language: Английский

Citations

17
An increment of diversity method for cell state trajectory inference of time-series scRNA-seq data DOI Creative Commons
Yan Hong, Hanshuang Li, Chunshen Long

et al.

Fundamental Research, Journal Year: 2024, Volume and Issue: 4(4), P. 770 - 776

Published: Feb. 9, 2024

The increasing emergence of the time-series single-cell RNA sequencing (scRNA-seq) data, inferring developmental trajectory by connecting transcriptome similar cell states (i.e., types or clusters) has become a major challenge. Most existing computational methods are designed for individual cells and do not take into account available time series information. We present IDTI based on Increment Diversity Trajectory Inference, which combines information minimum increment diversity method to infer state scRNA-seq data. apply simulated three real diverse tissue development datasets, compare it with six other commonly used inference in terms topology similarity branching accuracy. results have shown that accurately constructs without requirement starting cells. In performance test, we further demonstrate advantages high accuracy strong robustness.

Language: Английский

Citations

8
DELVE: feature selection for preserving biological trajectories in single-cell data DOI Creative Commons
Jolene S. Ranek, Wayne Stallaert, J. Justin Milner

et al.

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: March 29, 2024

Abstract Single-cell technologies can measure the expression of thousands molecular features in individual cells undergoing dynamic biological processes. While examining along a computationally-ordered pseudotime trajectory reveal how changes gene or protein impact cell fate, identifying such is challenging due to inherent noise single-cell data. Here, we present DELVE, an unsupervised feature selection method for representative subset which robustly recapitulate cellular trajectories. In contrast previous work, DELVE uses bottom-up approach mitigate effects confounding sources variation, and instead models states from modules based on core regulatory complexes. Using simulations, RNA sequencing, iterative immunofluorescence imaging data context cycle differentiation, demonstrate selects that better define cell-types cell-type transitions. available as open-source python package: https://github.com/jranek/delve .

Language: Английский

Citations

8