Evidence map›Paper›PMID 35867793›Full record

ArticleScience advances2022

Multiplexed high-throughput localized electroporation workflow with deep learning-based analysis for cell engineering.

Cesar A Patino, Nibir Pathak, Prithvijit Mukherjee, So Hyun Park, Gang Bao, Horacio D Espinosa

Abstract read
In one paragraph

Article in Science advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

  1. Article
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  7. The need for smart microalgal bioprospecting.Natural products and bioprospecting · 2025
    Review
  8. Article
  9. Article
  10. A small-molecule carrier for the intracellular delivery of a membrane-impermeable protein with retained bioactivity.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  11. Article
  12. Article
  13. Article
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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

6 authors.

Cesar A PatinoDepartment of Mechanical Engineering, Northwestern University, Evanston, IL 60208, USA.ORCID 0000-0003-3074-9314
Nibir PathakDepartment of Mechanical Engineering, Northwestern University, Evanston, IL 60208, USA.ORCID 0000-0003-3511-6022
Prithvijit MukherjeeDepartment of Mechanical Engineering, Northwestern University, Evanston, IL 60208, USA.
So Hyun ParkDepartment of Bioengineering, Rice University, 6500 Main St, Houston, TX 77030, USA.ORCID 0000-0003-3599-1687
Gang BaoDepartment of Bioengineering, Rice University, 6500 Main St, Houston, TX 77030, USA.ORCID 0000-0001-5501-554X
Horacio D EspinosaDepartment of Mechanical Engineering, Northwestern University, Evanston, IL 60208, USA.ORCID 0000-0002-1907-3213

Funding

HBB gene-editing for treating sickle cell diseaseR01HL152314 · NHLBI · RICE UNIVERSITY · PI BAO, GANG · 2020 to 2023
$2.5M
A Microfluidic Device for Temporal Live-Cell Analysis of Intracellular BiomoleculesR21GM132709 · NIGMS · NORTHWESTERN UNIVERSITY · PI ESPINOSA, HORACIO DANTE DANTE · 2019 to 2020
$416k
NHLBI NIH HHS R01 HL152314NIGMS NIH HHS R21 GM132709
6 · The paper itself

Abstract

Manipulation of cells for applications such as biomanufacturing and cell-based therapeutics involves introducing biomolecular cargoes into cells. However, successful delivery is a function of multiple experimental factors requiring several rounds of optimization. Here, we present a high-throughput multiwell-format localized electroporation device (LEPD) assisted by deep learning image analysis that enables quick optimization of experimental factors for efficient delivery. We showcase the versatility of the LEPD platform by successfully delivering biomolecules into different types of adherent and suspension cells. We also demonstrate multicargo delivery with tight dosage distribution and precise ratiometric control. Furthermore, we used the platform to achieve functional gene knockdown in human induced pluripotent stem cells and used the deep learning framework to analyze protein expression along with changes in cell morphology. Overall, we present a workflow that enables combinatorial experiments and rapid analysis for the optimization of intracellular delivery protocols required for genetic manipulation.

Indexed as

Deep LearningInduced Pluripotent Stem CellsCell EngineeringElectroporationHumansWorkflow

Identifiers

PMID35867793
PMCPMC9307252

What OpenQuestion holds

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LicenceCC BY-NC
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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.