Validation of the three-chamber strategy for studying mate choice in medaka

The three-chamber experiment, in which one test animal can choose between two animals placed in physically inaccessible compartments, is a widely adopted strategy for studying sexual preference in animals. Medaka, a small freshwater teleost, is an emerging model for dissecting the neurological/physiological mechanisms underlying mate choice for which intriguing findings have been accumulating. The three-chamber strategy has rarely been adopted in this species; therefore, here we investigated its validity using medaka colour variants that mate assortatively. First, a total of 551 movies, in which a test male and two choice females interacted for 30 min under a free-swimming condition, were manually analysed. The sexual preference of the males, calculated as a courtship ratio, was highly consistent between human observers (r > 0.96), supporting the objectivity of this manual-counting strategy. Second, we tested two types of three-chamber apparatuses, in which choice fish were presented in either a face-to-face or side-by-side location. Test fish (regardless of sex) spent most of the time associating with choice fish in the compartments. However, their sexual preference, calculated as an association ratio, was poorly reproduced when the locations of the choice fish were swapped. Third, the sexual preferences of males quantified using the manual-counting and either of the three-chamber strategies did not correlate (r = 0.147 or 0.297). Hence, we concluded that, even for individuals of a species like medaka, which spawn every day, sexual preference could not be reliably evaluated using the three-chamber strategy. Optimization of the protocol may solve this problem; however, the explanation for the observation that animals that are ready for spawning persist with never-accessible mating partners must be reconsidered.

MAAPER: model-based analysis of alternative polyadenylation using 3' end-linked reads

Most eukaryotic genes harbor multiple cleavage and polyadenylation sites (PASs), leading to expression of alternative polyadenylation (APA) isoforms. APA regulation has been implicated in a diverse array of physiological and pathological conditions. While RNA sequencing tools that generate reads containing the PAS, named onSite reads, have been instrumental in identifying PASs, they have not been widely used. By contrast, a growing number of methods generate reads that are close to the PAS, named nearSite reads, including the 3' end counting strategy commonly used in single cell analysis. How these nearSite reads can be used for APA analysis, however, is poorly studied. Here, we present a computational method, named model-based analysis of alternative polyadenylation using 3' end-linked reads (MAAPER), to examine APA using nearSite reads. MAAPER uses a probabilistic model to predict PASs for nearSite reads with high accuracy and sensitivity, and examines different types of APA events, including those in 3'UTRs and introns, with robust statistics. We show MAAPER's accuracy with data from both bulk and single cell RNA samples and its applicability in unpaired or paired experimental designs. Our result also highlights the importance of using well annotated PASs for nearSite read analysis.

Seqpac: A New Framework for small RNA analysis in R using Sequence-Based Counts

Small RNA sequencing (sRNA-seq) has become important for studying regulatory mechanisms in many cellular processes. Data analysis remains challenging, mainly because each class of sRNA--such as miRNA, piRNA, tRNA- and rRNA- derived fragments (tRFs/rRFs)--needs special considerations. Analysis therefore involves complex workflows across multiple programming languages, which can produce research bottlenecks and transparency issues. To make analysis of sRNA more accessible and transparent we present seqpac: a tool for advanced group-based analysis of sRNA completely integrated in R. This opens advanced sRNA analysis for Windows users--from adaptor trimming to visualization. Seqpac provides a framework of functions for analyzing a PAC object, which contains 3 standardized tables: sample phenotypic information (P), sequence annotations (A), and a counts table with unique sequences across the experiment (C). By applying a sequence-based counting strategy that maintains the integrity of the fastq sequence, seqpac increases flexibility and transparency compared to other workflows. It also contains an innovative targeting system allowing sequence counts to be summarized and visualized across sample groups and sequence classifications. Reanalyzing published data, we show that seqpac's fastq trimming performs equal to standard software outside R and demonstrate how sequence-based counting detects previously unreported bias. Applying seqpac to new experimental data, we discovered a novel rRF that was down-regulated by RNA pol I inhibition (anticancer treatment), and up-regulated in previously published data from tumor positive patients. Seqpac is available on github (https://github.com/Danis102/seqpac), runs on multiple platforms (Windows/Linux/Mac), and is provided with a step-by-step vignette on how to analyze sRNA-seq data.

Single-cell RNA counting at allele- and isoform-resolution using Smart-seq3

Large-scale sequencing of RNAs from individual cells can reveal patterns of gene, isoform and allelic expression across cell types and states1. However, current single-cell RNA-sequencing (scRNA-seq) methods have limited ability to count RNAs at allele- and isoform resolution, and long-read sequencing techniques lack the depth required for large-scale applications across cells2,3. Here, we introduce Smart-seq3 that combines full-length transcriptome coverage with a 5’ unique molecular identifier (UMI) RNA counting strategy that enabled in silico reconstruction of thousands of RNA molecules per cell. Importantly, a large portion of counted and reconstructed RNA molecules could be directly assigned to specific isoforms and allelic origin, and we identified significant transcript isoform regulation in mouse strains and human cell types. Moreover, Smart-seq3 showed a dramatic increase in sensitivity and typically detected thousands more genes per cell than Smart-seq2. Altogether, we developed a short-read sequencing strategy for single-cell RNA counting at isoform and allele-resolution applicable to large-scale characterization of cell types and states across tissues and organisms.