Evidence map›Paper›PMID 38645720›Full record

ArticleCurrent opinion in toxicology2024

Unique challenges and best practices for single cell transcriptomic analysis in toxicology.

David Filipovic, Omar Kana, Daniel Marri, Sudin Bhattacharya

Abstract read
In one paragraph

Article in Current opinion in toxicology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

4 authors.

David FilipovicInstitute for Quantitative Health Science & Engineering, East Lansing, MI, 48824, USA.
Omar KanaInstitute for Quantitative Health Science & Engineering, East Lansing, MI, 48824, USA.
Daniel MarriInstitute for Quantitative Health Science & Engineering, East Lansing, MI, 48824, USA.
Sudin BhattacharyaInstitute for Quantitative Health Science & Engineering, East Lansing, MI, 48824, USA.

Funding

VOLATILE ORGANIC CONTAMINANTS--QUANTIFYING THEIR MOVEMENT IN THE UNSATURATED ZONEP42ES004911 · NIEHS · MICHIGAN STATE UNIVERSITY · PI Brian P. Johnson · 1989 to 2026
$64.9M
Integrative transcriptional and epigenomic modeling of xenobiotic-activated gene regulatory networksR01ES031937 · NIEHS · MICHIGAN STATE UNIVERSITY · PI BHATTACHARYA, SUDIN, CUDDAPAH, SURESH · 2020 to 2024
$2.4M
NIEHS NIH HHS P42 ES004911NIEHS NIH HHS R01 ES031937
6 · The paper itself

Abstract

The application and analysis of single-cell transcriptomics in toxicology presents unique challenges. These include identifying cell sub-populations sensitive to perturbation; interpreting dynamic shifts in cell type proportions in response to chemical exposures; and performing differential expression analysis in dose-response studies spanning multiple treatment conditions. This review examines these challenges while presenting best practices for critical single cell analysis tasks. This covers areas such as cell type identification; analysis of differential cell type abundance; differential gene expression; and cellular trajectories. Towards enhancing the use of single-cell transcriptomics in toxicology, this review aims to address key challenges in this field and offer practical analytical solutions. Overall, applying appropriate bioinformatic techniques to single-cell transcriptomic data can yield valuable insights into cellular responses to toxic exposures.

Identifiers

PMID38645720
PMCPMC11027889

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Registered trials

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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.