Evidence map›Paper›PMID 40169572›Full record

ArticleLight, science & applications2025

Large-scale combinatorial optical barcoding of cells with laser particles.

Nicola Martino, Hao Yan, Geoffrey Abbott, Marissa Fahlberg, Sarah Forward, Kwon-Hyeon Kim, Yue Wu, Han Zhu, Sheldon J J Kwok, Seok-Hyun Yun

Abstract read
In one paragraph

Article in Light, science & applications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

10 authors.

Nicola Martino *Harvard Medical School and Wellman Center for Photomedicine, Massachusetts General Hospital, Cambridge, MA, 02139, USA.ORCID http://orcid.org/0000-0002-4639-2930
Hao Yan *Harvard Medical School and Wellman Center for Photomedicine, Massachusetts General Hospital, Cambridge, MA, 02139, USA.
Geoffrey AbbottLASE Innovation Inc., Waltham, MA, 02451, USA.
Marissa FahlbergLASE Innovation Inc., Waltham, MA, 02451, USA.
Sarah ForwardLASE Innovation Inc., Waltham, MA, 02451, USA.
Kwon-Hyeon KimHarvard Medical School and Wellman Center for Photomedicine, Massachusetts General Hospital, Cambridge, MA, 02139, USA.
Yue WuHarvard Medical School and Wellman Center for Photomedicine, Massachusetts General Hospital, Cambridge, MA, 02139, USA.
Han ZhuLASE Innovation Inc., Waltham, MA, 02451, USA.
Sheldon J J KwokLASE Innovation Inc., Waltham, MA, 02451, USA.
Seok-Hyun YunHarvard Medical School and Wellman Center for Photomedicine, Massachusetts General Hospital, Cambridge, MA, 02139, USA. syun@mgh.harvard.edu.

Funding

Laser particles for multi-dimensional single-cell analysisR01EB033155 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI YUN, SEOK-HYUN ANDY · 2021 to 2025
$3.5M
Multi-color laser particles for high-throughput pooled analysisR01EB034687 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI Seok-Hyun Andy Yun · 2023 to 2026
$2.0M
Time-lapse Flow Cytometry for Kinetic Profiling of T-Cell FunctionR44CA281529 · NCI · LASE INNOVATION INC. · PI KWOK, SHELDON J.J. · 2023 to 2024
$1.8M
Laser particles-based spatiotemporal and dynamic single-cell multiomicsK99HG013129 · NHGRI · MASSACHUSETTS GENERAL HOSPITAL · PI WU, YUE JANE · 2023 to 2024
$272k
NCI NIH HHS R44 CA281529NHGRI NIH HHS K99 HG013129NIBIB NIH HHS R01 EB033155NIBIB NIH HHS R01 EB034687U.S. Department of Health & Human Services | National Institutes of Health (NIH) R01-EB033155U.S. Department of Health & Human Services | National Institutes of Health (NIH) R01-EB034687U.S. Department of Health & Human Services | National Institutes of Health (NIH) R44CA281529
6 · The paper itself

Abstract

The identification of individual cells is crucial for advancements in single-cell analysis. Optically readable barcodes provide a means to distinguish and track cells through repeated, non-destructive measurements. Traditional fluorophore-based methods are limited by the finite number of unique barcodes they can produce. Laser particles (LPs), which emit narrowband peaks over a wide spectral range, have emerged as a promising technology for single-cell barcoding. Here, we demonstrate the use of multiple LPs to generate combinatorial barcodes, enabling the identification of a vast number of live cells. We introduce a theoretical framework for estimating the number of LPs required for unique barcodes and the expected identification error rate. Additionally, we present an improved LP-tagging method that is highly effective across a variety of cell types and evaluate its biocompatibility. Our experimental results show successful barcoding of several million cells, closely matching our theoretical predictions. This research marks a significant step forward in the scalability of LP technology for single-cell tracking and analysis.

Identifiers

PMID40169572
PMCPMC11962087

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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.