Evidence map›Paper›PMID 42026130›Full record

ArticleNature biotechnology2026

High-fidelity intravital imaging of biological dynamics with latent-space-enhanced digital adaptive optics.

Yunmin Zeng, Qi Zhang, Yihong Xiao, Shidong Wu, Seonghoon Kim, Yunhao Zhang, Mingrui Wang, Yuanlong Zhang, Xinyang Li, Zhi Lu and 2 more

Abstract read
PubMed Publisher
In one paragraph

Article in Nature biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Yunmin ZengDepartment of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China.ORCID http://orcid.org/0009-0005-4082-4391
Qi ZhangDepartment of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China.
Yihong XiaoCollege of Artificial Intelligence, Tsinghua University, Beijing, China.ORCID http://orcid.org/0009-0001-1840-8114
Shidong WuSchool of Information Science and Technology, Fudan University, Shanghai, China.
Seonghoon KimDepartment of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China.ORCID http://orcid.org/0000-0002-1145-0336
Yunhao ZhangWeiyang College, Tsinghua University, Beijing, China.
Mingrui WangDepartment of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China.ORCID http://orcid.org/0009-0008-8816-2018
Yuanlong ZhangInstitute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China.ORCID http://orcid.org/0000-0003-3725-1973
Xinyang LiInstitute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China.ORCID http://orcid.org/0000-0003-3880-5448
Zhi LuDepartment of Psychological and Cognitive Sciences, Tsinghua University, Beijing, China. luzhi@tsinghua.edu.cn.ORCID http://orcid.org/0000-0003-3739-8756
Jiamin WuDepartment of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China. wujiamin@tsinghua.edu.cn.ORCID http://orcid.org/0000-0003-3479-1026
Qionghai DaiInstitute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China. qhdai@tsinghua.edu.cn.ORCID http://orcid.org/0000-0001-7043-3061

Funding

China Postdoctoral Science Foundation 2023M741963National Natural Science Foundation of China (National Science Foundation of China) 62088102National Natural Science Foundation of China (National Science Foundation of China) 62222508National Natural Science Foundation of China (National Science Foundation of China) 62525506National Natural Science Foundation of China (National Science Foundation of China) 62575154Natural Science Foundation of Beijing Municipality (Beijing Natural Science Foundation) JR25021Natural Science Foundation of Beijing Municipality (Beijing Natural Science Foundation) Z240011
6 · The paper itself

Abstract

Intravital fluorescence microscopy is hampered by optical aberrations arising from heterogeneous distributions of the refractive index. Adaptive optics (AO) methods are either costly and slow, requiring additional hardware, or inaccurate due to lack of wavefront information in multiple angular directions. Here we present a latent-space-enhanced digital AO (LEAO) method that uses wave-optics priors embedded in high-dimensional spatial-angular data and semantically disentangles their representations in the latent space. LEAO achieves more than sixfold higher aberration estimation accuracy than the existing approach (coordinate-based neural representations for computational AO). It also exhibits strong robustness across different system configurations and imaging conditions, achieving almost an order of magnitude higher accuracy than iterative digital AO under extreme conditions such as a low signal-to-noise ratio of 3.4 dB. We experimentally demonstrate that LEAO improves diverse biological observations in vivo, such as large-scale tracking of T cells across an entire lymph node, multiregional neural recording in mouse cortex and long-term monitoring of neutrophil activation, extravasation and clearing through mouse intact skull after traumatic brain injury.

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.