Evidence map›Paper›PMID 37914751›Full record

ArticleScientific reports2023

Extended methods for spatial cell classification with DBSCAN-CellX.

Leonie Küchenhoff, Pascal Lukas, Camila Metz-Zumaran, Paul Rothhaar, Alessia Ruggieri, Volker Lohmann, Thomas Höfer, Megan L Stanifer, Steeve Boulant, Soheil Rastgou Talemi and 1 more

Abstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Quantifying bristle cell organization inbioRxiv : the preprint server for biology · 2025
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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

11 authors.

Leonie Küchenhoff *BioQuant-Center for Quantitative Biology, Heidelberg University, 69120, Heidelberg, Germany.
Pascal Lukas *BioQuant-Center for Quantitative Biology, Heidelberg University, 69120, Heidelberg, Germany.
Camila Metz-ZumaranDepartment of Molecular Genetics and Microbiology, College of Medicine, University of Florida, Gainesville, FL, USA.
Paul RothhaarDepartment of Infectious Diseases, Virology, Center for Integrative Infectious Disease Research (CIID), Heidelberg University, 69120, Heidelberg, Germany.
Alessia RuggieriDepartment of Infectious Diseases, Virology, Center for Integrative Infectious Disease Research (CIID), Heidelberg University, 69120, Heidelberg, Germany.
Volker LohmannDepartment of Infectious Diseases, Virology, Center for Integrative Infectious Disease Research (CIID), Heidelberg University, 69120, Heidelberg, Germany.
Thomas HöferDivision of Theoretical Systems Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Megan L StaniferDepartment of Molecular Genetics and Microbiology, College of Medicine, University of Florida, Gainesville, FL, USA.
Steeve BoulantDepartment of Molecular Genetics and Microbiology, College of Medicine, University of Florida, Gainesville, FL, USA.
Soheil Rastgou TalemiDepartment of Infectious Diseases, Virology, Center for Integrative Infectious Disease Research (CIID), Heidelberg University, 69120, Heidelberg, Germany.
Frederik GrawBioQuant-Center for Quantitative Biology, Heidelberg University, 69120, Heidelberg, Germany. frederik.graw@fau.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Local cell densities and positioning within cellular monolayers and stratified epithelia have important implications for cell interactions and the functionality of various biological processes. To analyze the relationship between cell localization and tissue physiology, density-based clustering algorithms, such as DBSCAN, allow for a detailed characterization of the spatial distribution and positioning of individual cells. However, these methods rely on predefined parameters that influence the outcome of the analysis. With varying cell densities in cell cultures or tissues impacting cell sizes and, thus, cellular proximities, these parameters need to be carefully chosen. In addition, standard DBSCAN approaches generally come short in appropriately identifying individual cell positions. We therefore developed three extensions to the standard DBSCAN-algorithm that provide: (i) an automated parameter identification to reliably identify cell clusters, (ii) an improved identification of cluster edges; and (iii) an improved characterization of the relative positioning of cells within clusters. We apply our novel methods, which are provided as a user-friendly OpenSource-software package (DBSCAN-CellX), to cellular monolayers of different cell lines. Thereby, we show the importance of the developed extensions for the appropriate analysis of cell culture experiments to determine the relationship between cell localization and tissue physiology.

Indexed as

AlgorithmsSoftwareCell SizeCluster Analysis

Identifiers

PMID37914751
PMCPMC10620226

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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.