Evidence map›Paper›PMID 38151595›Full record

ArticleNature methods2024

Thermal-plex: fluidic-free, rapid sequential multiplexed imaging with DNA-encoded thermal channels.

Fan Hong, Jocelyn Y Kishi, Ryan N Delgado, Jiyoun Jeong, Sinem K Saka, Hanquan Su, Constance L Cepko, Peng Yin

Abstract read
In one paragraph

Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Enzymatic DNA Reaction Networks for Orchestrating Stimuli-Dependent Temporal Molecular Pulse.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
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  7. Image-based 3D genomics through chromatin tracing.Nature reviews. Methods primers · 2024
    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

8 authors.

Fan HongWyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA.ORCID http://orcid.org/0000-0002-3370-7833
Jocelyn Y KishiWyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA.ORCID http://orcid.org/0000-0003-4352-1159
Ryan N DelgadoDepartments of Genetics and Ophthalmology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.
Jiyoun JeongWyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA.
Sinem K SakaWyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA.ORCID http://orcid.org/0000-0002-5017-379X
Hanquan SuWyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA.
Constance L CepkoDepartments of Genetics and Ophthalmology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0002-9945-6387
Peng YinWyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA. py@hms.harvard.edu.ORCID http://orcid.org/0000-0002-2769-6357

Funding

High-throughput single-molecule protein identification via super-resolution imagingDP1GM133052 · NIGMS · HARVARD UNIVERSITY · PI YIN, PENG · 2018 to 2022
$6.6M
Platform technologies for scalable highly multiplexed proteomic phenotyping of the brainRF1MH128861 · NIMH · HARVARD UNIVERSITY · PI CHUNG, KWANGHUN, DONG, HONG-WEI · 2021 to 2021
$5.5M
Development of a scalable strategy for reconstructing cell-type determined connectome of the mammalian brainRF1MH124611 · NIMH · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI CAI, DAWEN, CUI, MENG · 2020 to 2020
$3.9M
Multiplexed Nanoscale Protein Mapping Through Expansion Microscopy and Immuno-SABERRF1MH124606 · NIMH · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI BOYDEN, EDWARD S., YIN, PENG · 2020 to 2020
$2.7M
Decoding chromosome structure with multiplexed super-resolution microscopyR01GM124401 · NIGMS · HARVARD UNIVERSITY · PI YIN, PENG · 2018 to 2021
$2.5M
Thermal-plex: fluidic-free, rapid sequential multiplexed imaging of RNA and protein in brain tissuesRF1MH132599 · NIMH · HARVARD UNIVERSITY · PI YIN, PENG · 2023 to 2023
$2.5M
High-Throughput, Highly Multiplexed In Situ Proteomic Imaging of Human TissuesUH3CA255133 · NCI · HARVARD UNIVERSITY · PI YIN, PENG · 2020 to 2021
$1.1M
High-Throughput, Highly Multiplexed In Situ Proteomic Imaging of Human TissuesUG3HL145600 · NHLBI · HARVARD UNIVERSITY · PI YIN, PENG · 2018 to 2019
$833k
NCI NIH HHS UH3 CA255133NHLBI NIH HHS UG3 HL145600NIGMS NIH HHS DP1 GM133052NIGMS NIH HHS R01 GM124401NIMH NIH HHS RF1 MH124606NIMH NIH HHS RF1 MH124611NIMH NIH HHS RF1 MH128861NIMH NIH HHS RF1 MH132599
6 · The paper itself

Abstract

Multiplexed fluorescence imaging is typically limited to three- to five-plex on standard setups. Sequential imaging methods based on iterative labeling and imaging enable practical higher multiplexing, but generally require a complex fluidic setup with several rounds of slow buffer exchange (tens of minutes to an hour for each exchange step). We report the thermal-plex method, which removes complex and slow buffer exchange steps and provides fluidic-free, rapid sequential imaging. Thermal-plex uses simple DNA probes that are engineered to fluoresce sequentially when, and only when, activated with transient exposure to heating spikes at designated temperatures (thermal channels). Channel switching is fast (<30 s) and is achieved with a commercially available and affordable on-scope heating device. We demonstrate 15-plex RNA imaging (five thermal × three fluorescence channels) in fixed cells and retina tissues in less than 4 min, without using buffer exchange or fluidics. Thermal-plex introduces a new labeling method for efficient sequential multiplexed imaging.

Indexed as

DNAOptical ImagingRNATemperatureDNARNA

Identifiers

PMID38151595
PMCPMC10864187

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.