Evidence map›Paper›PMID 42458053›Full record

ArticleNature biotechnology2026

High-fidelity fast fluorescence lifetime imaging by event-based denoising.

Yiliang Zhou, Yihong Xiao, Jing Zhou, Bo Liu, Zhifeng Zhao, Xinyang Li, Minghuan Wang, Jiamin Wu, Qionghai Dai

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Article in Nature biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Yiliang Zhou *Department of Automation, Tsinghua University, Beijing, China.
Yihong Xiao *College of AI, Tsinghua University, Beijing, China.ORCID http://orcid.org/0009-0001-1840-8114
Jing Zhou *Department of Automation, Tsinghua University, Beijing, China.
Bo LiuChangping Laboratory, Beijing, China.ORCID http://orcid.org/0000-0002-9596-7307
Zhifeng ZhaoDepartment of Automation, Tsinghua University, Beijing, China.
Xinyang LiCollege of AI, Tsinghua University, Beijing, China.ORCID http://orcid.org/0000-0003-3880-5448
Minghuan WangDepartment of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Jiamin WuDepartment of Automation, Tsinghua University, Beijing, China. wujiamin@tsinghua.edu.cn.ORCID http://orcid.org/0000-0003-3479-1026
Qionghai DaiDepartment of Automation, Tsinghua University, Beijing, China. qhdai@tsinghua.edu.cn.ORCID http://orcid.org/0000-0001-7043-3061

Funding

National Natural Science Foundation of China (National Science Foundation of China) 62088102National Natural Science Foundation of China (National Science Foundation of China) 62525506Natural 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

Fluorescence lifetime imaging microscopy (FLIM) enables quantitative measurement of molecular environments, interactions and protein conformations. However, a large number of photons are required for accurate lifetime determination, restricting its practical applications in fast deep-tissue imaging. Here, we present event-based first-photon FLIM (EFLIM), a self-supervised denoising method that infers fluorescence lifetime at extremely low light. By representing each excitation event as a binary process instead of histogram accumulation, EFLIM reduces photon requirement by over two orders of magnitude compared to state-of-the-art algorithms, leading to an apparent mean lifetime measurement below one photon per pixel with strong robustness to intensity artifacts. To demonstrate EFLIM's applicability, we observed transient intracellular dynamics of ligand-dependent molecular states, captured putative vesicle-mediated contacts between different lymphocytes through multiplexed imaging in a single spectral channel and achieved rapid label-free visualization of tumor heterogeneity in human glioma tissue. These results illustrate EFLIM's strong potential in neuroscience, cell biology, immunology and pathology by probing dynamic molecular processes in vivo.

Identifiers

PMID42458053

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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.