Evidence map›Paper›PMID 38668136›Full record

ReviewMethods and protocols2024

Imaging Flow Cytometry: Development, Present Applications, and Future Challenges.

Savvas Dimitriadis, Lefkothea Dova, Ioannis Kotsianidis, Eleftheria Hatzimichael, Eleni Kapsali, Georgios S Markopoulos

Abstract readReview
In one paragraph

Review in Methods and protocols, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Review
  5. Article
  6. Article
  7. Review
  8. Review
  9. Article
  10. 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

6 authors.

Savvas DimitriadisHematology Laboratory, Unit of Molecular Biology and Translational Flow Cytometry, University Hospital of Ioannina, 45100 Ioannina, Greece.
Lefkothea DovaHematology Laboratory, Unit of Molecular Biology and Translational Flow Cytometry, University Hospital of Ioannina, 45100 Ioannina, Greece.
Ioannis KotsianidisDepartment of Hematology, University Hospital of Alexandroupolis, Democritus University of Thrace, 69100 Alexandroupolis, Greece.ORCID 0000-0001-7668-0186
Eleftheria HatzimichaelDepartment of Hematology, Faculty of Medicine, University of Ioannina, 45110 Ioannina, Greece.ORCID 0000-0002-4408-5646
Eleni KapsaliDepartment of Hematology, Faculty of Medicine, University of Ioannina, 45110 Ioannina, Greece.
Georgios S MarkopoulosHematology Laboratory, Unit of Molecular Biology and Translational Flow Cytometry, University Hospital of Ioannina, 45100 Ioannina, Greece.ORCID 0000-0002-7700-7003

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Imaging flow cytometry (ImFC) represents a significant technological advancement in the field of cytometry, effectively merging the high-throughput capabilities of flow analysis with the detailed imaging characteristics of microscopy. In our comprehensive review, we adopt a historical perspective to chart the development of ImFC, highlighting its origins and current state of the art and forecasting potential future advancements. The genesis of ImFC stemmed from merging the hydraulic system of a flow cytometer with advanced camera technology. This synergistic coupling facilitates the morphological analysis of cell populations at a high-throughput scale, effectively evolving the landscape of cytometry. Nevertheless, ImFC's implementation has encountered hurdles, particularly in developing software capable of managing its sophisticated data acquisition and analysis needs. The scale and complexity of the data generated by ImFC necessitate the creation of novel analytical tools that can effectively manage and interpret these data, thus allowing us to unlock the full potential of ImFC. Notably, artificial intelligence (AI) algorithms have begun to be applied to ImFC, offering promise for enhancing its analytical capabilities. The adaptability and learning capacity of AI may prove to be essential in knowledge mining from the high-dimensional data produced by ImFC, potentially enabling more accurate analyses. Looking forward, we project that ImFC may become an indispensable tool, not only in research laboratories, but also in clinical settings. Given the unique combination of high-throughput cytometry and detailed imaging offered by ImFC, we foresee a critical role for this technology in the next generation of scientific research and diagnostics. As such, we encourage both current and future scientists to consider the integration of ImFC as an addition to their research toolkit and clinical diagnostic routine.

Indexed as

artificial intelligenceflow cytometryimaging flow cytometryphenotype analysis

Identifiers

PMID38668136
PMCPMC11054958

What Socratic 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 Socratic graph.