{
  "abstract": "Single-cell RNA sequencing has supplied cell atlases and artificial intelligence (AI)-enabled virtual-cell models with scalable, gene-level descriptions of cellular state. Yet prevalent poly(A)-based counting assays capture only part of the RNA landscape. Here, \"single-cell whole-transcriptome detection\" denotes sequencing-based efforts to detect a broader range of RNA biotypes than conventional poly(A)-based gene counting; no assay detects or quantifies every cellular RNA species without bias. We compare RNA-capture scope, transcript-structure resolution, temporal labeling, spatial context, and additional modalities as distinct measurement dimensions. From a biosensing perspective, analytical sensitivity, transcript coverage, throughput, and assay bias determine which signals are available to computational models. We then examine cell-state representations, regulatory networks, dynamics, perturbation prediction, and foundation models. Total-RNA assays reveal additional non-coding RNAs, long-read methods inform transcript structure, and temporal assays constrain RNA kinetics. Whether these signals improve prediction beyond conventional gene-count inputs requires matched, task-specific model comparisons. We distinguish molecular observability, statistical association, predictive performance, and causal validation, which requires appropriate interventions. We propose benchmarks controlling sequencing depth, cost, model architecture, and held-out samples while testing feature ablations, out-of-distribution performance, uncertainty calibration, and experimental outcomes. Together, high-throughput poly(A)-based profiling and broader RNA measurements provide complementary cellular breadth and regulatory depth.",
  "abstract_shown": true,
  "affiliations": [
    "State Key Laboratory of Industrial Control Technology, Research Center for Analytical Instrumentation, Institute of Cyber-Systems and Control, College of Control Science and Engineering, Zhejiang University, Hangzhou 310000, China."
  ],
  "article_type": "review article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Xudong Liu",
      "orcid": "0009-0005-6628-5795"
    },
    {
      "affs": [
        0
      ],
      "name": "Bo Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Kefei Zou"
    },
    {
      "affs": [
        0
      ],
      "name": "Ruikai Wan"
    },
    {
      "affs": [
        0
      ],
      "name": "Qiangyuan Zhu",
      "orcid": "0000-0003-4148-442X"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8z7731tywrje5.bib",
  "cited_by": 0,
  "doi": "10.3390/bios16100581",
  "fulltext_url": "https://europepmc.org/articles/PMC13702128",
  "id": "8z7731tywrje5",
  "issns": [
    "2079-6374"
  ],
  "landing_url": "https://europepmc.org/article/MED/42930128",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7222512270",
  "pdf_url": "https://europepmc.org/articles/PMC13702128?pdf=render",
  "pmcid": "PMC13702128",
  "pmid": "42930128",
  "published": "2026-10-16",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8z7731tywrje5.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:01Z",
      "native_id": "MED:42930128",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42930128"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Single-cell and spatial transcriptomics",
      "subfield": "molecular-biology"
    }
  ],
  "title": "Single-Cell Whole-Transcriptome Detection Expands the Observable RNA Space for AI Virtual-Cell Models.",
  "title_text": "Single-Cell Whole-Transcriptome Detection Expands the Observable RNA Space for AI Virtual-Cell Models.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:22:01Z",
  "url": "https://research.draconitic.com/paper/8z7731tywrje5",
  "venue": "Biosensors",
  "versions": []
}