Single-Cell Whole-Transcriptome Detection Expands the Observable RNA Space for AI Virtual-Cell Models.
- 1State 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.
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.
Related
- Joint representation of spliced and unspliced RNA expands information content in single-cell expression data.
- Integrated transcriptome and single-cell RNA sequencing identifies transient receptor potential channel-related biomarkers in ulcerative colitis
- Whole-transcriptome-scale isoform-resolved spatial imaging of single cells in tissues
- Single-cell transcriptome profiling of post-treatment and treatment-naïve colorectal cancer: Insights into putative mechanisms of chemoresistance
- AI and computational methods for reconstructing cell-state-specific gene regulatory networks from single-cell transcriptome and chromatin accessibility data in cancer: methods, applications, and perspectives
BibTeX
@article{liu2026singlecell,
title = {{Single-Cell Whole-Transcriptome Detection Expands the Observable RNA Space for AI Virtual-Cell Models.}},
author = {Xudong Liu and Bo Zhang and Kefei Zou and Ruikai Wan and Qiangyuan Zhu},
journal = {Biosensors},
year = {2026},
month = oct,
doi = {10.3390/bios16100581},
url = {https://europepmc.org/article/MED/42930128},
note = {CC BY},
}
RIS
TY - JOUR
TI - Single-Cell Whole-Transcriptome Detection Expands the Observable RNA Space for AI Virtual-Cell Models.
AU - Xudong Liu
AU - Bo Zhang
AU - Kefei Zou
AU - Ruikai Wan
AU - Qiangyuan Zhu
PY - 2026
DA - 2026/10/16
JO - Biosensors
DO - 10.3390/bios16100581
UR - https://europepmc.org/article/MED/42930128
AB - 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.
SN - 2079-6374
ER -