Thursday, May 28, 2026

PANDORA-seq – a new way to assess sperm quality

PANDORA-seq reveals sncRNA landscape in human sperm, facilitating sperm quality assessment

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Characterization and comparison of human sperm sncRNA signatures between traditional sncRNA-seq and PANDORA-seq — image:
(A) Comparison of the relative ratio of clean reads to raw reads and reads aligned to the genome to clean reads between traditional sncRNA-seq and PANDORA-seq. (B) The scatter plots depicting the correlation of miRNA, tsRNA, and rsRNA expression profiles of paired traditional sncRNA-seq and PANDORA-seq. Spearman correlation coefficients and P-values are shown. (C) Comparison of the relative expression levels of four major sncRNA origins (miRNA, tsRNA, rsRNA, ysRNA, and piRNA) in healthy human sperm determined by the PANDORA-seq protocol. (D) Expression levels of miRNAs, tsRNAs, and rsRNAs in human sperm samples validated by northern blotting. (E) The radar plot showing the different relative expression proportions of each tsRNA subcategory with respect to the two protocols. (F) Northern blotting validation of tsRNALeu(TAG), tsRNALys(TTT), and tsRNAGly(GCC) in human sperm. (G) Visualization of the proportional distribution of cyto-tsRNAs and mt-tsRNAs in healthy human sperm determined by the PANDORA-seq protocol. (H) The radar plots illustrating the relative expression proportions of each tsRNA subcategory in relation to three distinct tsRNA origins (5′, inner′, and 3′). (I) Sequence mapping location (left), expression profile (middle), and reverse transcription PCR validation (right) of tsRNAAla(AGC), tsRNAArg(TCG), and tsRNAGlu(CTC) in human sperm. (J) Distribution of expression proportions for six nucleus-encoded rsRNAs and two mitochondria-encoded rsRNAs. (K) The boxplots showing the relative expression of five nucleus-encoded rsRNAs and two mitochondria-encoded rsRNA categories. (L) Visualization of expression signature and sequence mapping location of rsRNA-18S and rsRNA-28S in human sperm samples. (M) Northern blotting validation of rsRNA-5.8S, selected rsRNA-18S main peaks (peak #1, peak #2), and selected rsRNA-28S main peaks (peak #1, peak #2) in human sperm samples.
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Credit: Ruofan Huang, Yiting Yang, Wenlin Jiang, Zheng Cao, Junchao Shi, Xiao-Ou Zhang, Yunfang Zhang

The global fertility crisis is increasingly attributed to a steady decline in human semen quality, with conditions such as asthenozoospermia (reduced sperm motility) and teratozoospermia (abnormal sperm morphology) accounting for more than half of male subfertility cases.

While small noncoding RNAs (sncRNAs) are known to be abundant in mature sperm and essential for regulating spermatogenesis, traditional sequencing methods have predominantly focused on miRNAs, which represent less than 1% of the total sncRNA population in sperm, whereas transfer RNA-derived small RNAs (tsRNAs) and ribosomal RNA-derived small RNAs (rsRNAs) comprise the majority of the sperm sncRNA profile. These sncRNAs frequently possess chemical modifications and non-canonical terminal structures that hinder adapter ligation and reverse transcription during standard library preparation, thereby making their detection challenging with conventional methods.

In a prospective cohort study published in Genes & Diseases, researchers from Tongji University, Shanghai Institute for Biomedical and Pharmaceutical Technologies (SIBPT), and Chinese Academy of Sciences utilized PANDORA-seq—a panoramic RNA display strategy that employs a two-step enzymatic treatment with T4 polynucleotide kinase (T4PNK) and α-ketoglutarate-dependent dioxygenase (AlkB)—to remove these inhibitory modifications. By applying this method to a cohort of 25 participants categorized into normozoospermia (NZS), asthenozoospermia (AZS), and teratozoospermia (TZS) groups, the researchers generated one of the most comprehensive landscapes of human sperm sncRNAs to date.

The study revealed that tsRNAs and rsRNAs are not only the dominant species, constituting over 97% of the total small RNA population, but are also strongly correlated with clinical indicators of sperm quality, such as motility and morphology. Among rsRNAs, the majority are derived from cytoplasmic 28S and 18S subunits, with 28S-derived sequences alone accounting for over 74% of the rsRNA population. tsRNAs also exhibit distinct patterns, with nuclear-encoded species primarily originating from the 5' end of tRNAs, whereas mitochondrial-encoded tsRNAs are skewed toward internal cleavage sites.

Functional analysis identified robust linear correlations between these specific molecular species and clinical indicators: nuclear-encoded tsRNA-Phe and tsRNA-Lys are positively correlated with progressive motility (PR), whereas rsRNA-28S exhibits a significant negative correlation with motility parameters. Furthermore, rsRNA-5.8S showed a notable negative correlation with both the head shape index (TZI) and the percentage of intact sperm heads, suggesting a potential mechanistic role in regulating sperm morphology.

Conversely, tsRNA species such as tsRNA-iMet, tsRNA-Val, and various 28S-derived rsRNAs were negatively correlated with motility, indicating their association with subfertile states. While correlations between sncRNAs and morphology were generally less pronounced, rsRNA-5.8S remained negatively associated with intact head and head shape indices, and tsRNA-Val showed a positive association with abnormal morphological indices.

To translate these molecular findings into clinical utility, the researchers employed machine learning and LASSO regression based on sperm rsRNA and tsRNA profiles, establishing the male subfertility sncRNA signature (MSsncSig), the AZS-related signature (AZSsncSig), and the TZS-related signature (TZSsncSig). These models demonstrated exceptional diagnostic power, achieving area under the curve (AUC) scores of 0.83 or higher. This predictive capability represents a substantial improvement over traditional WHO semen quality assessments, providing a novel molecular framework for diagnosing male infertility.

In conclusion, PANDORA-seq provides critical insights into the landscape of the human sperm sncRNA repertoire, identifying tsRNAs and rsRNAs as pivotal markers of reproductive health. By establishing correlations between these modified RNAs and sperm fitness, this research offers a robust framework for assessing sperm quality and understanding the molecular mechanisms underlying male subfertility and its potential intergenerational impacts.

Reference

Title of the original paper: PANDORA-seq reveals human sperm sncRNA signature endowed with sperm quality assessment

Journal: Genes & Diseases

Genes & Diseases is a journal for molecular and translational medicine. The journal primarily focuses on publishing investigations on the molecular bases and experimental therapeutics of human diseases. Publication formats include full length research article, review article, short communication, correspondence, perspectives, commentary, views on news, and research watch.

DOI: https://doi.org/10.1016/j.gendis.2025.101807