Evidence map›Paper›PMID 31186477›Full record

ArticleScientific reports2019

Evaluation of ultra-low input RNA sequencing for the study of human T cell transcriptome.

Jingya Wang, Sadiye Amcaoglu Rieder, Jincheng Wu, Susana Hayes, Rebecca A Halpin, Melissa de Los Reyes, Yashaswi Shrestha, Roland Kolbeck, Rajiv Raja

Abstract read
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Dissecting the Immune System through Gene Regulation.Advances in experimental medicine and biology · 2024
    Article
  10. Article
  11. Article
  12. Article
  13. Metabolic and functional impairment of CD8Journal of leukocyte biology · 2022
    Article
  14. Article
  15. Review
  16. Article
  17. 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

9 authors.

Jingya WangTranslational Science and Experimental Medicine, Early Respiratory, Inflammation and Autoimmunity, R&D Biopharmaceuticals, AstraZeneca, Gaithersburg, USA. wangjin@medimmune.com.
Sadiye Amcaoglu RiederBiosciences, Early Respiratory, Inflammation and Autoimmunity, R&D Biopharmaceuticals, AstraZeneca, Gaithersburg, USA.
Jincheng WuResearch Bioinformatics, MedImmune AstraZeneca, Gaithersburg, USA.
Susana HayesTranslational Medicine, Oncology R&D, AstraZeneca, Gaithersburg, USA.
Rebecca A HalpinTranslational Medicine, Oncology R&D, AstraZeneca, Gaithersburg, USA.
Melissa de Los ReyesTranslational Medicine, Oncology R&D, AstraZeneca, Gaithersburg, USA.
Yashaswi ShresthaTranslational Medicine, Oncology R&D, AstraZeneca, Gaithersburg, USA.ORCID http://orcid.org/0000-0002-5567-0277
Roland KolbeckRespiratory, Inflammation and Autoimmunity, MedImmune AstraZeneca, Gaithersburg, USA.
Rajiv RajaTranslational Medicine, Oncology R&D, AstraZeneca, Gaithersburg, USA.ORCID http://orcid.org/0000-0002-4663-0951

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Deeper understanding of T cell biology is crucial for the development of new therapeutics. Human naïve T cells have low RNA content and their numbers can be limiting; therefore we set out to determine the parameters for robust ultra-low input RNA sequencing. We performed transcriptome profiling at different cell inputs and compared three protocols: Switching Mechanism at 5' End of RNA Template technology (SMART) with two different library preparation methods (Nextera and Clontech), and AmpliSeq technology. As the cell input decreased the number of detected coding genes decreased with SMART, while stayed constant with AmpliSeq. However, SMART enables detection of non-coding genes, which is not feasible for AmpliSeq. The detection is dependent on gene abundance, but not transcript length. The consistency between technical replicates and cell inputs was comparable across methods above 1 K but highly variable at 100 cell input. Sensitivity of detection for differentially expressed genes decreased dramatically with decreased cell inputs in all protocols, support that additional approaches, such as pathway enrichment, are important for data interpretation at ultra-low input. Finally, T cell activation signature was detected at 1 K cell input and above in all protocols, with AmpliSeq showing better detection at 100 cells.

Indexed as

Exome SequencingGene Expression ProfilingHigh-Throughput Nucleotide SequencingHumansRNA, MessengerSequence Analysis, RNAT-LymphocytesTranscriptomeRNA, Messenger

Identifiers

PMID31186477
PMCPMC6559993

What OpenQuestion holds

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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.