Evidence map›Paper›PMID 42308418›Full record

ReviewBriefings in bioinformatics2026

Current trends and challenges in deciphering single molecule resolution maps of single cell transcriptomes.

David Schaeper, Upol Chowdhury, Sarath Chandra Janga

Abstract readReview
In one paragraph

Review in Briefings in bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

David SchaeperDepartment of Biomedical Engineering and Informatics, Luddy School of Informatics, Computing and Engineering, Indiana University Indianapolis (IU Indianapolis), 535 West Michigan Street, Indianapolis, IN 46202, United States.ORCID 0009-0003-6374-7478
Upol ChowdhuryDepartment of Biomedical Engineering and Informatics, Luddy School of Informatics, Computing and Engineering, Indiana University Indianapolis (IU Indianapolis), 535 West Michigan Street, Indianapolis, IN 46202, United States.
Sarath Chandra JangaDepartment of Biomedical Engineering and Informatics, Luddy School of Informatics, Computing and Engineering, Indiana University Indianapolis (IU Indianapolis), 535 West Michigan Street, Indianapolis, IN 46202, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Advancements in single-cell RNA sequencing (scRNA-seq) techniques have expanded the study of cellular heterogeneity and transcriptional dynamics. Early methods relied on manual cell isolation followed by barcode introduction, but subsequent approaches integrated automated cell isolation with cellular barcoding to increase throughput. While most current single-cell RNA-seq methods aim to capture transcripts at the single-cell level in a high-throughput manner using short-read sequencing, such efforts frequently prevent assignment of full-length transcripts to individual cells, limiting insight into isoform diversity and complete mutational profiles. Recent advances in long-read sequencing accuracy are starting to enable integration of full-length transcript coverage with high-throughput barcoding. This review traces the evolution of scRNA-seq from early manual isolation methods to today's high-throughput short-read droplet- and combinatorial barcoding-based platforms. Then, the review discusses recent advances stemming from the adaptation of high-throughput scRNA-seq protocols for use with long-read sequencing and addresses key challenges such as accurate barcode identification despite lower base-calling accuracy and efforts to compensate for reduced throughput relative to short-read technologies. In parallel, the review highlights the development of computational tools tailored to long-read scRNA-seq, including methods for cell barcode and unique molecular index recovery, variant detection, and complete end-to-end workflows, emphasizing both their shared and unique advantages. Finally, applications of long-read scRNA-seq are shown to provide novel insights, spanning cancer genomics, neurology, early development, and disease contexts. By integrating technical, computational, and biological perspectives, the transformative potential of long-read scRNA-seq is shown, advancing our understanding of cellular heterogeneity.

Indexed as

Single-Cell AnalysisTranscriptomeAnimalsGene Expression ProfilingHigh-Throughput Nucleotide SequencingHumansSequence Analysis, RNASingle-Cell Gene Expression Analysiscomputational workflowsisoform resolutionlong-read sequencingprecision medicinesingle cell transcriptomessingle molecule

Identifiers

PMID42308418
PMCPMC13274977

What OpenQuestion holds

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LicenceCC BY-NC
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.