Alpha1 Record ID: FADDB351
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Huang, K.; Zhou, Y.; Wang, T.; Li, X.; Zhao, X.; Liu, X.; Huang, L.; Zhou, X.; Liu, J.
RNA velocity is a computational framework for single-cell RNA sequencing (scRNA-seq) that estimates the future transcriptional state of individual cells, thereby capturing the direction and rate of cell state transitions rather than providing a purely static snapshot. Since its introduction in 2018, multiple RNA velocity methods have been developed, differing in their modeling assumptions, required inputs, computational complexity, and robustness. However, there remains limited consensus on how best to evaluate these methods or on which tools are most reliable under specific biological and technical settings. Here, we perform a systematic comparison of 29 velocity inference algorithms across 114 simulated datasets with known ground-truth cell dynamics and 62 real scRNA-seq datasets, and we extend the evaluation to spatial and multi-omics levels where velocity is increasingly applied. We benchmark RNA velocity methods using a unified framework that decomposes performance into four practical dimensions: accuracy, scalability, stability, and usability. Our results show that performance rankings vary substantially across metrics and datasets, indicating no single method is uniformly optimal and that practical deployment is often constrained by feasibility and robustness as much as by accuracy. Based on these results, we provide actionable guidance for selecting RNA velocity tools according to data modality, available priors, and computational constraints. Finally, we identify key bottlenecks that currently limit RNA velocity development and deployment, including scalability to large size of datasets, sensitivity to gene selection, and the lack of genuinely multimodal and spatially explicit velocity models for spot-based technologies.
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CD4⁺ T cells confer transplantable rejuvenation via Rivers of telomeres
Lanna, A.; Valvo, S.; Dustin, M.; Rinaldi, F.
Using a GPT-5-driven autonomous lab to optimize the cost and titer of cell-free protein synthesis
Smith, A. A.; Wong, E. L.; Donovan, R. C.; Chapman, B. A.; Harry, R.; Tirandazi, P.; Kanigowska, P.; Gendreau, E. A.; Dahl, R. H.; Jastrzebski, M.; Cortez, J. E.; Bremner, C. J.; Hemuda, J. C. M.; Dooner, J.; Graves, I.; Karandikar, R.; Lionetti, C.; Christopher, K.; Consiglio, A. L.; Tran, A.; McCusker, W.; Nguyen, D. X.; Nunes da Silva, I. B.; Bautista-Ayala, A. R.; McNerney, M. P.; Atkins, S.; McDuffie, M.; Serber, W.; Barber, B. P.; Thanongsinh, T.; Nesson, A.; Lama, B.; Nichols, B.; LaFrance, C.; Nyima, T.; Byrn, A.; Thornhill, R.; Cai, B.; Ayala-Valdez, L.; Wong, A.; Che, A. J.; Thavaraj
A Single-Cell and Spatial 3D Multi-omic Atlas of Developing Human Basal Ganglia and Inhibitory Neurons
Heffel, M. G.; Xu, H.; Pastor-Alonso, O.; Li, X.; Baig, M. S.; Irfan Ghoor, R.; Li, R.; Kern, C.; Kum, J.; Zhang, Y.; Paino, J.; Tsai, M. J.; Tai, C.-Y.; Tucker, G.; Zhao, Z.; Hou, A.; von Behren, Z.; Bhade, M.; Li, S.; Sandoval, K.; Scholes, J.; Codrea, F.; Calimlim, J.; Liao, E. K.; Leung, G.; Kim, J.; Eskin, E.; Flint, J.; Cotter, J. A.; Pasaniuc, B.; Bintu, B.; Zhu, Q.; Mukamel, E. A.; Ernst, J.; Paredes, M. F.; Luo, C.
Prediction of transformative breakthroughs in biomedical research
Davis, M. T.; Busse, B. L.; Arabi, S.; Meyer, P.; Hoppe, T. A.; Meseroll, R. A.; Hutchins, B. I.; Willis, K. A.; Santangelo, G. M.