Evidence map›Paper›PMID 42761285›Full record

ArticlePNAS nexus2026

Video-rate label-free molecular mapping in living plant tissue with a deployable optical encoder.

Alexander Ingold, Richard G Baird, Dasmeet Kaur, Nidhi Dwivedi, Reed Sorenson, Leslie Sieburth, Chang-Jun Liu, Rajesh Menon

Abstract read
In one paragraph

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

0numbers the graph read from it
0cells of the map it votes in
0citing 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

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

8 authors.

Alexander IngoldDepartment of Electrical & Computer Engineering, University of Utah, Salt Lake City, UT 84112, USA.ORCID https://orcid.org/0009-0007-5238-0016
Richard G BairdDepartment of Electrical & Computer Engineering, University of Utah, Salt Lake City, UT 84112, USA.ORCID https://orcid.org/0009-0008-0098-3226
Dasmeet KaurBiological, Environmental & Climate Sciences Department, Brookhaven National Laboratory, Upton, NY 11973, USA.
Nidhi DwivediBiological, Environmental & Climate Sciences Department, Brookhaven National Laboratory, Upton, NY 11973, USA.ORCID https://orcid.org/0000-0003-1148-5579
Reed SorensonDepartment of Biology, University of Utah, Salt Lake City, UT 84112, USA.ORCID https://orcid.org/0000-0001-8650-0601
Leslie SieburthDepartment of Biology, University of Utah, Salt Lake City, UT 84112, USA.ORCID https://orcid.org/0000-0001-6691-2000
Chang-Jun LiuBiological, Environmental & Climate Sciences Department, Brookhaven National Laboratory, Upton, NY 11973, USA.ORCID https://orcid.org/0000-0001-6189-8756
Rajesh MenonDepartment of Electrical & Computer Engineering, University of Utah, Salt Lake City, UT 84112, USA.ORCID https://orcid.org/0000-0002-4777-376X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Living tissues contain dynamic biochemical information that is difficult to capture with conventional hyperspectral microscopes because sequential spectral acquisition is poorly matched to in vivo molecular processes that evolve during measurement. Here, we introduce a task-specific optical encoding framework for video-rate molecular inference in living plant tissue. The system integrates a passive spectral encoder, implemented here as a low-angle scattering low-density polyethylene (LDPE) layer, into a 22-mm miniaturized probe and learns a supervised mapping from ultraviolet-excited autofluorescence measurements to biomolecular abundance maps. Unlike conventional pipelines that first reconstruct hyperspectral datacubes and then perform spectral unmixing, the deployed system directly estimates endogenous molecular contrast associated primarily with lignin and chlorophyll in poplar tissue, with additional suberin-associated contrast evaluated in suberin-rich tissue. This reframing makes the measurement task biomolecular inference rather than spectral reconstruction, enabling biochemical mapping under low-photon autofluorescence conditions while reducing data burden and computational latency. In living poplar stems, the platform captures autofluorescence-derived videos of embolism propagation and wound-induced biochemical remodeling, dynamic processes for which sequential spectral acquisition can introduce temporal mixing because the molecular contrast evolves during the scan itself. The system also resolves genotype-dependent reductions in lignin-associated autofluorescence in engineered poplar lines. Direct molecular inference improves biomolecular estimation relative to a reconstruction-based pipeline, while probabilistic decoding provides uncertainty estimates. These results show that compact passive spectral encoding, when optimized for biological inference rather than datacube recovery, enables deployable, label-free molecular videography of living plant tissue dynamics after task-specific calibration.

Indexed as

autofluorescencecomputational imaginghyperspectral imaginglabel-free molecular imagingplant phenotyping

Identifiers

PMID42761285
PMCPMC13586757

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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.