Evidence map›Paper›PMID 35173045›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2022

A high-throughput technique to map cell images to cell positions using a 3D imaging flow cytometer.

Zunming Zhang, Rui Tang, Xinyu Chen, Lauren Waller, Alston Kau, Anthony A Fung, Bien Gutierrez, Cheolhong An, Sung Hwan Cho, Lingyan Shi and 1 more

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Receptor-Mediated SPION Labeling of CD4Nanomaterials (Basel, Switzerland) · 2025
    Article
  5. Review
  6. Highly Efficient Calibration-Free Color Compensation Algorithm for Imaging Flow Cytometry.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2025
    Article
  7. Review
  8. Review
  9. Article
  10. Article
  11. Review
  12. Article
  13. Review
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

11 authors.

Zunming ZhangDepartment of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093.
Rui TangDepartment of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093.ORCID 0000-0001-7024-3831
Xinyu ChenDepartment of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093.
Lauren WallerDepartment of Bioengineering, University of California San Diego, La Jolla, CA 92093.
Alston KauNanoCellect Biomedical Inc., San Diego, CA 92121.
Anthony A FungDepartment of Bioengineering, University of California San Diego, La Jolla, CA 92093.ORCID 0000-0003-1631-7451
Bien GutierrezNanoCellect Biomedical Inc., San Diego, CA 92121.
Cheolhong AnDepartment of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093.
Sung Hwan ChoNanoCellect Biomedical Inc., San Diego, CA 92121.
Lingyan ShiDepartment of Bioengineering, University of California San Diego, La Jolla, CA 92093.ORCID 0000-0003-1373-3206
Yu-Hwa LoDepartment of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093; ylo@ucsd.edu.ORCID 0000-0003-4602-1627

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We develop a high-throughput technique to relate positions of individual cells to their three-dimensional (3D) imaging features with single-cell resolution. The technique is particularly suitable for nonadherent cells where existing spatial biology methodologies relating cell properties to their positions in a solid tissue do not apply. Our design consists of two parts, as follows: recording 3D cell images at high throughput (500 to 1,000 cells/s) using a custom 3D imaging flow cytometer (3D-IFC) and dispensing cells in a first-in-first-out (FIFO) manner using a robotic cell placement platform (CPP). To prevent errors due to violations of the FIFO principle, we invented a method that uses marker beads and DNA sequencing software to detect errors. Experiments with human cancer cell lines demonstrate the feasibility of mapping 3D side scattering and fluorescent images, as well as two-dimensional (2D) transmission images of cells to their locations on the membrane filter for around 100,000 cells in less than 10 min. While the current work uses our specially designed 3D imaging flow cytometer to produce 3D cell images, our methodology can support other imaging modalities. The technology and method form a bridge between single-cell image analysis and single-cell molecular analysis.

Indexed as

Flow CytometryHigh-Throughput Screening AssaysHumansImage Processing, Computer-AssistedImaging, Three-DimensionalSoftware3D imaging flow cytometerdisease diagnosishigh throughputsingle cellspatial biology

Identifiers

PMID35173045
PMCPMC8872737

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.