Single-Cell Whole-Transcriptome Detection Expands the Observable RNA Space for AI Virtual-Cell Models

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.