Evidence map›Paper›PMID 40256918›Full record

ArticleSmall methods2025

Modular Droplet-Based Microfluidic Platform for Functional Phenotypic Screening of Natural Killer Cells.

Florian Aubermann, Senne Seneca, Tomáš Hofman, Irene Garcés-Lázaro, Karim Ajmail, Kai Daubner, Adelheid Cerwenka, Ilia Platzman, Joachim P Spatz

Abstract read
In one paragraph

Article in Small methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Machine vision-enabled ODThe Analyst · 2026
    Article
  2. Review
  3. Review
  4. 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.

Florian AubermannDepartment of Cellular Biophysics, Max Planck Institute for Medical Research, 69120, Heidelberg, Germany.ORCID https://orcid.org/0000-0003-4504-7710
Senne SenecaDepartment of Cellular Biophysics, Max Planck Institute for Medical Research, 69120, Heidelberg, Germany.ORCID https://orcid.org/0000-0002-9939-9917
Tomáš HofmanDepartment of Immunobiochemistry, Mannheim Institute for Innate Immunoscience (MI3), Medical Faculty Mannheim, Heidelberg University, 68167, Mannheim, Germany.ORCID https://orcid.org/0000-0003-2574-9686
Irene Garcés-LázaroDepartment of Immunobiochemistry, Mannheim Institute for Innate Immunoscience (MI3), Medical Faculty Mannheim, Heidelberg University, 68167, Mannheim, Germany.ORCID https://orcid.org/0000-0003-0834-082X
Karim AjmailDepartment of Cellular Biophysics, Max Planck Institute for Medical Research, 69120, Heidelberg, Germany.ORCID https://orcid.org/0009-0005-8492-6023
Kai DaubnerDepartment of Cellular Biophysics, Max Planck Institute for Medical Research, 69120, Heidelberg, Germany.ORCID https://orcid.org/0009-0002-5384-6467
Adelheid CerwenkaDepartment of Immunobiochemistry, Mannheim Institute for Innate Immunoscience (MI3), Medical Faculty Mannheim, Heidelberg University, 68167, Mannheim, Germany.ORCID https://orcid.org/0000-0001-6977-3536
Ilia PlatzmanDepartment of Cellular Biophysics, Max Planck Institute for Medical Research, 69120, Heidelberg, Germany.ORCID https://orcid.org/0000-0003-1239-7458
Joachim P SpatzDepartment of Cellular Biophysics, Max Planck Institute for Medical Research, 69120, Heidelberg, Germany.ORCID https://orcid.org/0000-0003-3419-9807

Funding

Bundesministerium für Bildung und ForschungDeutsche Forschungsgemeinschaft 394046768-SFB1366Deutsche Forschungsgemeinschaft ExcellenceCluster3DMatterMadetoOrder(EXC-2082/1-390761711)Deutsche Forschungsgemeinschaft RTG2727 - 445549683Deutsche Forschungsgemeinschaft SFB-TRR156Deutsche Forschungsgemeinschaft SPP1937Deutsche Forschungsgemeinschaft TRR179Deutsche Krebshilfe 74114180
6 · The paper itself

Abstract

Natural Killer (NK) cells, as key effector cells of the innate immune system, display high heterogeneity in their ability to kill target cells. The underlying mechanisms remain poorly understood. Here, a droplet-based microfluidic platform is presented to identify and select NK cells with serial killing ability. To this end, primary human NK cells are encapsulated with several target cells using an efficient negative pressure-based droplet generator. Capitalizing on the large number of possible killing events due to quantization into droplets, a convolutional neural network analysis pipeline is developed to quantify the cytotoxicity and abundance of serial killing events with high accuracy. To physically select NK cells based on their serial killing ability, MultiCell-Sort - an advanced real-time image-based droplet sorting module - is presented. While conventional single-cell sorters mostly evaluate intrinsically-encoded properties, such as protein expression levels, MultiCell-Sort can select living NK cells based on complex functional phenotypes emerging from multiple cell-cell interactions within the droplet. This novel microfluidic phenotyping platform hereby allows the potential integration of complementary techniques to provide an understanding of regulators and markers underlying the heterogeneous nature of NK cell functional phenotypes.

Indexed as

Killer Cells, NaturalLab-On-A-Chip DevicesMicrofluidic Analytical TechniquesMicrofluidicsHumansNeural Networks, ComputerPhenotypedroplet‐based microfluidicsnatural killer cell phenotypesnegative pressuresupervised deep learning

Identifiers

PMID40256918
PMCPMC12391620

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.