Evidence map›Paper›PMID 33990801›Full record

ArticleNature protocols2021

High-throughput full-length single-cell RNA-seq automation.

Lira Mamanova, Zhichao Miao, Ayesha Jinat, Peter Ellis, Lesley Shirley, Sarah A Teichmann

Abstract readEvaluation Study
PubMed Publisher
In one paragraph

Article in Nature protocols, 2021. 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
1.9field-weighted citation impact, top 14% of its field
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, 19 citations in OpenAlex.

  1. Translational cancer research · 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

6 authors at 3 institutions in 2 countries.

Lira Mamanova *Wellcome Sanger Institute, Cambridge, UK. lm4@sanger.ac.uk.ORCID http://orcid.org/0000-0003-1463-8622
Zhichao Miao *Wellcome Sanger Institute, Cambridge, UK.ORCID http://orcid.org/0000-0002-5777-9815
Ayesha JinatWellcome Sanger Institute, Cambridge, UK.
Peter EllisWellcome Sanger Institute, Cambridge, UK.
Lesley ShirleyWellcome Sanger Institute, Cambridge, UK.
Sarah A TeichmannWellcome Sanger Institute, Cambridge, UK. st9@sanger.ac.uk.ORCID http://orcid.org/0000-0002-6294-6366
Wellcome Sanger Institute · GBEuropean Bioinformatics Institute · GBUniversity of Cambridge · GB

Funding

Wellcome Trust 206194
6 · The paper itself

Abstract

Existing protocols for full-length single-cell RNA sequencing produce libraries of high complexity (thousands of distinct genes) with outstanding sensitivity and specificity of transcript quantification. These full-length libraries have the advantage of allowing probing of transcript isoforms, are informative regarding single-nucleotide polymorphisms and allow assembly of the VDJ region of the T- and B-cell-receptor sequences. Since full-length protocols are mostly plate-based at present, they are also suited to profiling cell types where cell numbers are limiting, such as rare cell types during development. A disadvantage of these methods has been the scalability and cost of the experiments, which has limited their popularity as compared with droplet-based and nanowell approaches. Here, we describe an automated protocol for full-length single-cell RNA sequencing, including both an in-house automated Smart-seq2 protocol and a commercial kit-based workflow. The protocols take 3-5 d to complete, depending on the number of plates processed in a batch. We discuss these two protocols in terms of ease of use, equipment requirements, running time, cost per sample and sequencing quality. By benchmarking the lysis buffers, reverse transcription enzymes and their combinations, we have optimized the in-house automated protocol to dramatically reduce its cost. An automated setup can be adopted easily by a competent researcher with basic laboratory skills and no prior automation experience. These pipelines have been employed successfully for several research projects allied with the Human Cell Atlas initiative ( www.humancellatlas.org ).

Indexed as

Automation, LaboratoryRNA-SeqSingle-Cell AnalysisAnimalsMice

Identifiers

PMID33990801
OpenAlexW3162247661

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.