Chung, N. C. | Search bioRxiv

bioRxiv2018

Real-time in vivo Global Transcriptional Dynamics During Plasmodium falciparum Blood-stage Development

Genome-wide analysis of transcription in the human malaria parasite Plasmodium falciparum has revealed robust variation in steady-state mRNA abundance throughout the 48-hour intraerythrocytic developmental cycle (IDC) suggesting that this process is highly dynamic and tightly regulated. However, the precise timing of mRNA transcription and decay remains poorly understood due to the utilization of methods that only measure total RNA and cannot differentiate between newly transcribed, decaying and stable cellular RNAs. Here we utilize rapid 4-thiouracil (4-TU) incorporation via pyrimidine salvage to specifically label, capture and quantify newly-synthesized P. falciparum RNA transcripts at every hour throughout the IDC following erythrocyte invasion. This high resolution global analysis of the transcriptome captures the timing and rate of transcription for each newly synthesized mRNA in vivo, revealing active transcription throughout all stages of the IDC. To determine the fraction of active transcription and/or transcript stabilization contributing to the total mRNA abundance at each timepoint we have generated a statistical model to fit the data for each gene which reveals varying degrees of transcription and stabilization for each mRNA corresponding to developmental transitions and independent of abundance profile. Finally, our results provide new insight into co-regulation of mRNAs throughout the IDC through regulatory DNA sequence motifs associated with these processes, thereby expanding our understanding of P. falciparum mRNA dynamics.

microbiology↗

bioRxiv2018

Statistical Significance of Cluster Membership

Single cell RNA sequencing (scRNA-seq) allows us to dissect transcriptional heterogeneity arising from cellular types, spatio-temporal contexts, and environmental stimuli. Cell identities of samples derived from heterogeneous subpopulations are routinely determined by clustering of scRNA-seq data. Computational cell identities are then used in downstream analysis, feature selection, and visualization. However, how can we examine if cell identities are accurately inferred? To this end, we introduce non-parametric methods to evaluate cell identities by testing cluster memberships of single cell samples in an unsupervised manner. We propose posterior inclusion probabilities for cluster memberships to select and visualize samples relevant to subpopulations. Beyond simulation studies, we examined two scRNA-seq data - a mixture of Jurkat and 293T cells and a large family of peripheral blood mononuclear cells. We demonstrated probabilistic feature selection and improved t-SNE visualization. By learning uncertainty in clustering, the proposed methods enable rigorous testing of cell identities in scRNA-seq.

genomics↗