Evidence map›Paper›PMID 31647675›Full record

ArticleNano letters2020

Massively-Parallelized, Deterministic Mechanoporation for Intracellular Delivery.

Harish G Dixit, Renate Starr, Morgan L Dundon, Pranee I Pairs, Xin Yang, Yanyan Zhang, Daniel Nampe, Christopher B Ballas, Hideaki Tsutsui, Stephen J Forman and 2 more

Abstract read
In one paragraph

Article in Nano letters, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing 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

31 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. The Rise of Mechanobiology for Advanced Cell Engineering and Manufacturing.Advanced materials (Deerfield Beach, Fla.) · 2025
    Review
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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

12 authors.

Harish G DixitDepartment of Bioengineering , University of California - Riverside , Riverside , California 92521 , United States.ORCID 0000-0003-3564-5709
Renate StarrDepartment of Cancer Immunotherapy and Tumor Immunology , City of Hope Beckman Research Institute and Medical Center , Duarte , California 91010 , United States.
Morgan L DundonMaterials Science and Engineering Program , University of California - Riverside , Riverside , California 92521 , United States.ORCID 0000-0002-6148-0664
Pranee I PairsMaterials Science and Engineering Program , University of California - Riverside , Riverside , California 92521 , United States.
Xin YangDepartment of Cancer Immunotherapy and Tumor Immunology , City of Hope Beckman Research Institute and Medical Center , Duarte , California 91010 , United States.
Yanyan ZhangDepartment of Mechanical Engineering , University of California - Riverside , Riverside , California 92521 , United States.
Daniel NampeDepartment of Bioengineering , University of California - Riverside , Riverside , California 92521 , United States.
Christopher B BallasDivision of Hematology/Oncology , Indiana University School of Medicine , Indianapolis , Indiana 46202 , United States.
Hideaki TsutsuiDepartment of Bioengineering , University of California - Riverside , Riverside , California 92521 , United States.ORCID 0000-0002-2558-2639
Stephen J FormanDepartment of Cancer Immunotherapy and Tumor Immunology , City of Hope Beckman Research Institute and Medical Center , Duarte , California 91010 , United States.
Christine E BrownDepartment of Cancer Immunotherapy and Tumor Immunology , City of Hope Beckman Research Institute and Medical Center , Duarte , California 91010 , United States.
Masaru P RaoDepartment of Bioengineering , University of California - Riverside , Riverside , California 92521 , United States.ORCID 0000-0002-5939-6332

Funding

Ultrahigh throughput cellular manipulation via massively parallel microinjectionR21RR026253 · NCRR · UNIVERSITY OF CALIFORNIA RIVERSIDE · PI BALLAS, CHRISTOPHER BRADLEY, RAO, MASARU PALAKURTHI · 2010 to 2011
$360k
Ultrahigh throughput cellular manipulation via massively parallel microinjectionR21GM103973 · NIGMS · UNIVERSITY OF CALIFORNIA RIVERSIDE · PI BALLAS, CHRISTOPHER BRADLEY, RAO, MASARU PALAKURTHI · 2012 to 2012
$183k
NCRR NIH HHS R21 RR026253NIGMS NIH HHS R21 GM103973
6 · The paper itself

Abstract

Microfluidic intracellular delivery approaches based on plasma membrane poration have shown promise for addressing the limitations of conventional cellular engineering techniques in a wide range of applications in biology and medicine. However, the inherent stochasticity of the poration process in many of these approaches often results in a trade-off between delivery efficiency and cellular viability, thus potentially limiting their utility. Herein, we present a novel microfluidic device concept that mitigates this trade-off by providing opportunity for deterministic mechanoporation (DMP) of cells en masse. This is achieved by the impingement of each cell upon a single needle-like penetrator during aspiration-based capture, followed by diffusive influx of exogenous cargo through the resulting membrane pore, once the cells are released by reversal of flow. Massive parallelization enables high throughput operation, while single-site poration allows for delivery of small and large-molecule cargos in difficult-to-transfect cells with efficiencies and viabilities that exceed both conventional and emerging transfection techniques. As such, DMP shows promise for advancing cellular engineering practice in general and engineered cell product manufacturing in particular.

Indexed as

Lab-On-A-Chip DevicesCell SurvivalCytoplasmDiffusionElectroporationHumansNeedlesTransfectioncellular biomanufacturingcellular engineeringex vivo cell therapyIntracellular deliverymechanoporationtransfection

Identifiers

PMID31647675
PMCPMC8210888

What OpenQuestion holds

Textmetadata
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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.