Evidence map›Paper›PMID 40963713›Full record

ArticleCellular and molecular bioengineering2025

Innovative Method for Fully Automated, Enzyme-Free Tissue Dissociation and Preparation for Single-Cell Analysis.

Sarah Planchak, E Celeste Welch, Benjamin Phelps, Joshua Phelps, Alejandra Hernandez Moyers, Kathryn Whitehead, John Murphy, Nikos Tapinos, Anubhav Tripathi

Abstract read
In one paragraph

Article in Cellular and molecular bioengineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

9 authors.

Sarah PlanchakCenter for Biomedical Engineering, School of Engineering, Brown University, Providence, RI 02912 USA.ORCID 0009-0005-0135-4542
E Celeste WelchCenter for Biomedical Engineering, School of Engineering, Brown University, Providence, RI 02912 USA.ORCID 0000-0003-0691-1449
Benjamin PhelpsCenter for Biomedical Engineering, School of Engineering, Brown University, Providence, RI 02912 USA.
Joshua PhelpsCenter for Biomedical Engineering, School of Engineering, Brown University, Providence, RI 02912 USA.
Alejandra Hernandez MoyersCenter for Biomedical Engineering, School of Engineering, Brown University, Providence, RI 02912 USA.
Kathryn WhiteheadCenter for Biomedical Engineering, School of Engineering, Brown University, Providence, RI 02912 USA.ORCID 0009-0001-1635-946X
John MurphyCenter for Biomedical Engineering, School of Engineering, Brown University, Providence, RI 02912 USA.
Nikos TapinosDepartment of Neurosurgery, Warren Alpert Medical School, Brown University, Providence, RI 02903 USA.ORCID 0000-0002-3798-5363
Anubhav TripathiCenter for Biomedical Engineering, School of Engineering, Brown University, Providence, RI 02912 USA.ORCID 0000-0002-8915-2320

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Tissue dissociation is a critical but often overlooked step in single-cell analysis, impacting data quality, reproducibility, and biological insights. Conventional enzymatic and mechanical dissociation methods introduce variability, damage cells, and alter transcriptomic profiles, compromising downstream applications. While the initial innovation in electrical dissociation was published, this work introduces expanded characterization, including bulk RNA sequencing, diverse tissue types, and improved flow cytometry. Methods: Here, we present a fully automated, enzyme-free method that integrates electric field-based dissociation with purification and centrifugation, providing a standardized, scalable alternative. A square wave oscillating electric field at 100 V/cm was used for dissociating tissue samples in 5 minutes or less. Results: The system rapidly and gently dissociated glioblastoma spheroids and mouse spleen tissue, achieving a 10 × increase in live cell yield compared to automated enzymatic and mechanical dissociation (gentleMACS) and a 96 ± 2% single-cell recovery rate in glioblastoma spheroids. Transcriptomic analysis revealed minimal gene expression changes post-dissociation, with an R Conclusions: By reducing operator variability, improving scalability, and maintaining cellular integrity, this technology offers a robust solution for high-throughput single-cell applications in diagnostics, drug discovery, and precision medicine. Supplementary Information: The online version contains supplementary material available at 10.1007/s12195-025-00850-5.

Indexed as

AutomationCell spheroidElectric fieldsRNA sequencingSingle-cell analysisTissue dissociation

Identifiers

PMID40963713
PMCPMC12436257

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.