Evidence map›Paper›PMID 40475448›Full record

ArticlebioRxiv : the preprint server for biology2025

Evaluating the practical aspects and performance of commercial single-cell RNA sequencing technologies.

Anna E Elz, Derrik Gratz, Annalyssa Long, David Sowerby, Azi Hadadianpour, Evan W Newell

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Derrik Gratz
Annalyssa Long
David Sowerby
Azi Hadadianpour
Evan W Newell

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid development of updated and new commercially available single-cell transcriptomics platforms provides users with a range of experimental options. Cost, sensitivity, throughput, flexibility, and ease of use, all influence the selection of an optimal workflow. We performed a comprehensive comparison of single-cell transcriptomic approaches using multiple standardized PBMCs from different donors. We report on standard single-cell metrics including cell recovery, sequencing efficiency, sensitivity, cell annotation, and differential gene expression for seven recently available kits that interrogate whole transcriptome mRNA, and two that include TCR profiling. We also discuss workflow throughput, sample requirements, timing, cost, and labor as critical factors to consider. In addition to variable experimental constraints imposed by each platform, our findings highlight differences in cell recovery and sensitivity, which we found to significantly influence the ability to resolve cell subtypes. This work provides a basis by which users can balance performance and practical considerations when selecting a single-cell RNA sequencing platform.

Identifiers

PMID40475448
PMCPMC12139828

What OpenQuestion holds

Textmetadata
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.