Evidence map›Paper›PMID 26856740›Full record

ArticleScientific reports2016

Automated single cell isolation from suspension with computer vision.

Rita Ungai-Salánki, Tamás Gerecsei, Péter Fürjes, Norbert Orgovan, Noémi Sándor, Eszter Holczer, Robert Horvath, Bálint Szabó

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
3.8field-weighted citation impact, top 7% of its field
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

16 citing papers in PubMed, 53 citations in OpenAlex.

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  13. Current Trends of Microfluidic Single-Cell Technologies.International journal of molecular sciences · 2018
    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

8 authors at 3 institutions in 1 country.

Rita Ungai-SalánkiDoctoral School of Molecular- and Nanotechnologies, University of Pannonia, Veszprém, Hungary.
Tamás GerecseiDepartment of Biological Physics, Eötvös University, Pázmány Péter sétány 1A, Budapest, H-1117 Hungary.
Péter FürjesMEMS Lab, Institute of Technical Physics and Materials Science, Centre for Energy Research, Hung. Acad. Sci., Budapest, Hungary.
Norbert OrgovanNanobiosensorics Group, Institute of Technical Physics and Materials Science, Centre for Energy Research, Hung. Acad. Sci., Budapest, Hungary.
Noémi SándorMTA-ELTE Immunology Research Group, Budapest, Hungary.
Eszter HolczerMEMS Lab, Institute of Technical Physics and Materials Science, Centre for Energy Research, Hung. Acad. Sci., Budapest, Hungary.
Robert HorvathNanobiosensorics Group, Institute of Technical Physics and Materials Science, Centre for Energy Research, Hung. Acad. Sci., Budapest, Hungary.
Bálint SzabóNanobiosensorics Group, Institute of Technical Physics and Materials Science, Centre for Energy Research, Hung. Acad. Sci., Budapest, Hungary.
Centre for Energy Research · HUEötvös Loránd University · HUNanobiosensorics Laboratory · HU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Current robots can manipulate only surface-attached cells seriously limiting the fields of their application for single cell handling. We developed a computer vision-based robot applying a motorized microscope and micropipette to recognize and gently isolate intact individual cells for subsequent analysis, e.g., DNA/RNA sequencing in 1-2 nanoliters from a thin (~100 μm) layer of cell suspension. It can retrieve rare cells, needs minimal sample preparation, and can be applied for virtually any tissue cell type. Combination of 1 μm positioning precision, adaptive cell targeting and below 1 nl liquid handling precision resulted in an unprecedented accuracy and efficiency in robotic single cell isolation. Single cells were injected either into the wells of a miniature plate with a sorting speed of 3 cells/min or into standard PCR tubes with 2 cells/min. We could isolate labeled cells also from dense cultures containing ~1,000 times more unlabeled cells by the successive application of the sorting process. We compared the efficiency of our method to that of single cell entrapment in microwells and subsequent sorting with the automated micropipette: the recovery rate of single cells was greatly improved.

Indexed as

Image Processing, Computer-AssistedCell SeparationFemaleHumansMaleRobotics

Identifiers

PMID26856740
PMCPMC4746594
OpenAlexW2263306484

What OpenQuestion holds

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LicenceCC BY
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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.