Hutton Alexandre, Meyer Jesse G
Department of Computational Biomedicine, Board of Governors Innovation Center, and Smidt Heart Institute Cedars Sinai Medical Center, Los Angeles CA, 90048, USA.
bioRxiv. 2025 May 13:2025.05.07.652753. doi: 10.1101/2025.05.07.652753.
Transient surges in gene or protein expression often mark the key regulatory checkpoints that propel cells from one functional state to the next, yet they are easy to miss in sparse, noisy single‑cell omics data. We introduce , a trajectory‑inference pipeline integrated into our cloud‑based single‑cell analysis platform PSCS. scTransient transforms single‑cell expression profiles into continuous pseudotime signals and couples them with wavelet‑based signal processing to isolate short‑lived but biologically meaningful bursts of activity. After ordering cells with unsupervised graph trajectories or supervised psupertime, windows expression values along pseudotime, applies a continuous wavelet transform, and assigns every gene a Transient‑Event Score (TES) that rewards sharp, isolated coefficients while penalizing background fluctuations. Synthetic benchmarks show TES robustly recovers transient events across a wide range of cell numbers, signal‑to‑noise ratios, and event widths. Applying scTransient to three public datasets-hematopoietic differentiation, monocyte‑to‑macrophage maturation, and single‑cell proteomic cell‑cycle progression-uncovers previously unreported, process‑specific expression spikes. These include erythropoiesis regulators (e.g., Nfe2), membrane‑raft remodeling proteins during macrophage differentiation, and S‑phase DNA‑replication factors in A549 cells. Functional enrichment confirms that top‑scoring genes cluster into pathways directly pertinent to each transition. By extending trajectory inference from descriptive ordering to quantitative detection of fleeting regulatory programs, scTransient-now readily accessible via the PSCS web interface-offers researchers a practical route to uncovering transient molecular events that drive development, differentiation, and disease.
基因或蛋白质表达的短暂激增通常标志着推动细胞从一种功能状态转变为另一种功能状态的关键调控检查点，但在稀疏、有噪声的单细胞组学数据中很容易被忽视。我们引入了scTransient，这是一种集成到我们基于云的单细胞分析平台PSCS中的轨迹推断管道。scTransient将单细胞表达谱转换为连续的伪时间信号，并将它们与基于小波的信号处理相结合，以分离短暂但具有生物学意义的活动爆发。在使用无监督图轨迹或有监督的伪时间对细胞进行排序后，scTransient沿着伪时间划分窗口表达值，应用连续小波变换，并为每个基因分配一个瞬态事件分数（TES），该分数对尖锐、孤立的系数给予奖励，同时对背景波动进行惩罚。合成基准表明，TES能够在广泛的细胞数量、信噪比和事件宽度范围内稳健地恢复瞬态事件。将scTransient应用于三个公共数据集——造血分化、单核细胞向巨噬细胞成熟以及单细胞蛋白质组细胞周期进程——发现了以前未报道的、特定于过程的表达峰值。这些包括红细胞生成调节因子（如Nfe2）、巨噬细胞分化过程中的膜筏重塑蛋白以及A549细胞中的S期DNA复制因子。功能富集证实，得分最高的基因聚集在与每个转变直接相关的途径中。通过将轨迹推断从描述性排序扩展到对短暂调控程序的定量检测，现在可以通过PSCS网络界面轻松访问的scTransient为研究人员提供了一条实用途径，以发现驱动发育、分化和疾病的瞬态分子事件。
