ArticleeLife2024
Long-read single-cell sequencing reveals expressions of hypermutation clusters of isoforms in human liver cancer cells.
Article in eLife, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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.
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.
Who cites it
7 citing papers in PubMed, 8 citations in OpenAlex.
- IFDlong: a model-based isoform and fusion detector for accurate annotation and quantification of long-read RNA-seq data.Genome biology · 2026Article
- mRNA Isoforms and Variants in Health and Disease.International journal of molecular sciences · 2025Review
- Deep Spatial Sequencing Revealing Differential Immune Responses in Human Hepatocellular Carcinoma.bioRxiv : the preprint server for biology · 2025Article
- Deep spatial sequencing revealing differential immune responses in human hepatocellular carcinoma.Frontiers in cell and developmental biology · 2025Article
- Utility analyses of AVITI sequencing chemistry.BMC genomics · 2024Article
- Utility Analyses of AVITI Sequencing Chemistry.bioRxiv : the preprint server for biology · 2024Article
- Article
Corrections and comments
- Update of
Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
The protein diversity of mammalian cells is determined by arrays of isoforms from genes. Genetic mutation is essential in species evolution and cancer development. Accurate long-read transcriptome sequencing at single-cell level is required to decipher the spectrum of protein expressions in mammalian organisms. In this report, we developed a synthetic long-read single-cell sequencing technology based on LOOPSeq technique. We applied this technology to analyze 447 transcriptomes of hepatocellular carcinoma (HCC) and benign liver from an individual. Through Uniform Manifold Approximation and Projection analysis, we identified a panel of mutation mRNA isoforms highly specific to HCC cells. The evolution pathways that led to the hyper-mutation clusters in single human leukocyte antigen molecules were identified. Novel fusion transcripts were detected. The combination of gene expressions, fusion gene transcripts, and mutation gene expressions significantly improved the classification of liver cancer cells versus benign hepatocytes. In conclusion, LOOPSeq single-cell technology may hold promise to provide a new level of precision analysis on the mammalian transcriptome.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.