BrainCellR: A Precise Cell Type Nomenclature Pipeline for Comparative Analysis Across Brain Single-Cell Datasets DOI Creative Commons
Yuhao Chi, Simone Marini, Guang‐Zhong Wang

и другие.

Computational and Structural Biotechnology Journal, Год журнала: 2024, Номер 23, С. 4306 - 4314

Опубликована: Ноя. 26, 2024

Single-cell studies in neuroscience require precise cell type classification and consistent nomenclature that allows for meaningful comparisons across diverse datasets. Current approaches often lack the ability to identify fine-grained types establish standardized annotations at cluster level, hindering comprehensive understanding of brain's cellular composition. To facilitate data integration multiple models datasets, we designed BrainCellR. This pipeline provides researchers with a powerful user-friendly tool efficient nomination from single-cell transcriptomic data. While initially focused on brain studies, BrainCellR is applicable other tissues complex compositions. goes beyond conventional by incorporating system level. feature enables comparable different promoting providing deeper insights into landscape brain. All documents BrainCellR, including source code, user manual tutorials, are freely available https://github.com/WangLab-SINH/BrainCellR.

Язык: Английский

Consensus representation of multiple cell–cell graphs from gene signaling pathways for cell type annotation DOI Creative Commons
Yu‐An Huang, Yue-Chao Li,

Zhu-Hong You

и другие.

BMC Biology, Год журнала: 2025, Номер 23(1)

Опубликована: Янв. 23, 2025

Язык: Английский

Процитировано

1
HIPPIE: A Multimodal Deep Learning Model for Electrophysiological Classification of Neurons DOI Creative Commons
Jesus Gonzalez-Ferrer, Julian Lehrer, H. Schweiger

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Март 15, 2025

Abstract Extracellular electrophysiological recordings present unique computational challenges for neuronal classification due to noise, technical variability, and batch effects across experimental systems. We introduce HIPPIE (High-dimensional Interpretation of Physiological Patterns In recordings), a deep learning framework that combines self-supervised pretraining on unlabeled datasets with supervised fine-tuning classify neurons from extracellular recordings. Using conditional convolutional joint autoencoders, learns robust, technology-adjusted representations waveforms spiking dynamics. This model can be applied clustering diverse biological cultures technologies. validated both in vivo mouse vitro brain slices, where it demonstrated superior performance over other unsupervised methods cell-type discrimination aligned closely anatomically defined classes. Its latent space organizes along gradients, while enabling individual corrected alignment experiments. establishes general systematically decoding diversity native engineered

Язык: Английский

Процитировано

0
Mapping Cell Identity from scRNA-seq: a primer on computational methods DOI Creative Commons
Daniele Traversa, Matteo Chiara

Computational and Structural Biotechnology Journal, Год журнала: 2025, Номер unknown

Опубликована: Апрель 1, 2025

Single cell (sc) technologies mark a conceptual and methodological breakthrough in our way to study cells, the base units of life. Thanks these technological developments, large-scale initiatives are currently ongoing aimed at mapping all types human body, with ambitious aim gain cell-level resolution physiological development disease. Since its broad applicability ease interpretation scRNA-seq is probably most common sc-based application. This assay uses high throughput RNA sequencing capture gene expression profiles sc-level. Subsequently, under assumption that differences transcriptional programs correspond distinct cellular identities, ad-hoc computational methods used infer from patterns. A wide array were developed for this task. However, depending on underlying algorithmic approach associated requirements, each method might have specific range application, implications not always clear end user. Here we will provide concise overview state-of-the-art identity annotation scRNA-seq, tailored new users non-computational scientists. To end, classify existing tools five main categories, discuss their key strengths, limitations

Язык: Английский

Процитировано

0
Toward automated and explainable high-throughput perturbation analysis in single cells DOI Creative Commons
Jesus Gonzalez-Ferrer, Mohammed A. Mostajo-Radji

Patterns, Год журнала: 2025, Номер 6(4), С. 101228 - 101228

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
Protocol for deep-learning-driven cell type label transfer in single-cell RNA sequencing data DOI

Zoe Zabetian,

Jesus Gonzalez-Ferrer, Julian Lehrer

и другие.

STAR Protocols, Год журнала: 2025, Номер 6(2), С. 103768 - 103768

Опубликована: Апрель 14, 2025

Язык: Английский

Процитировано

0
Intestinal organoids in inflammatory bowel disease: advances, applications, and future directions DOI Creative Commons
Jianzhen Ren,

Silin Huang

Frontiers in Cell and Developmental Biology, Год журнала: 2025, Номер 13

Опубликована: Май 12, 2025

Inflammatory bowel disease (IBD), characterized by chronic gastrointestinal inflammation, is a significant global health challenge. Traditional models often fail to accurately reflect human pathophysiology, leading suboptimal treatments. This review provides an overview of recent advancements in intestinal organoid technology and its role IBD research. Organoids, derived from patient-specific or pluripotent stem cells, retain the genetic, epigenetic, structural characteristics native gut, allowing for precise modeling key aspects IBD. Innovations CRISPR editing, organoid-microbe co-cultures, organ-on-a-chip systems have enhanced physiological relevance these models, facilitating drug discovery personalized therapy screening. However, challenges such as vascularization deficits need standardized protocols remain. underscores interdisciplinary efforts bridge gap between complex reality Future directions include development scalable vascularized robust regulatory frameworks accelerate therapeutic translation. Organoids hold promise unraveling heterogeneity transforming management.

Язык: Английский

Процитировано

0
QuickVol: a lightweight browser tool for immersive visualizations of volumetric data DOI Creative Commons
Maxim Kuznetsov, Mircea Teodorescu, Mohammed A. Mostajo-Radji

и другие.

iScience, Год журнала: 2024, Номер 27(12), С. 111379 - 111379

Опубликована: Ноя. 15, 2024

Volumetric layouts of data are becoming increasingly common in a number fields. Visualizing these often requires downloading large suite dedicated tools with significant learning curve. This process can be overwhelming for students or new researchers looking to quickly visualize and showcase volumetric dataset. QuickVol was developed as system allow rapid viewing without requiring extra setup. Built on WebGL, our run any modern web browser, including mobile browsers, work completely offline. Additionally, an experimental immersive hand-tracking feature is included, which allows hands-free manipulation the imported volume, along mode virtual reality headset.

Язык: Английский

Процитировано

0
BrainCellR: A Precise Cell Type Nomenclature Pipeline for Comparative Analysis Across Brain Single-Cell Datasets DOI Creative Commons
Yuhao Chi, Simone Marini, Guang‐Zhong Wang

и другие.

Computational and Structural Biotechnology Journal, Год журнала: 2024, Номер 23, С. 4306 - 4314

Опубликована: Ноя. 26, 2024

Single-cell studies in neuroscience require precise cell type classification and consistent nomenclature that allows for meaningful comparisons across diverse datasets. Current approaches often lack the ability to identify fine-grained types establish standardized annotations at cluster level, hindering comprehensive understanding of brain's cellular composition. To facilitate data integration multiple models datasets, we designed BrainCellR. This pipeline provides researchers with a powerful user-friendly tool efficient nomination from single-cell transcriptomic data. While initially focused on brain studies, BrainCellR is applicable other tissues complex compositions. goes beyond conventional by incorporating system level. feature enables comparable different promoting providing deeper insights into landscape brain. All documents BrainCellR, including source code, user manual tutorials, are freely available https://github.com/WangLab-SINH/BrainCellR.

Язык: Английский

Процитировано

0