RNA biomarker for sperm aging and quality

Abstract

Disclosed herein are methods for determining the quality of sperm in a sperm sample. The methods may include obtaining an RNA expression profile of the sperm using PANoramic RNA Display by Overcoming RNA modification Aborted sequencing (PANDORA-seq). This disclosure also relates to methods for performing an in vitro fertilization or artificial insemination procedure and obtaining an RNA expression profile of sperm in a sperm sample.

Description

[0001]RNA BIOMARKER FOR SPERM AGING AND QUALITY CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Provisional Patent Application No.63 / 683,570, filed on August 15, 2024, and U.S. Provisional Patent Application No.63 / 708,184, filed on October 16, 2024, each of which is incorporated by reference herein in its entirety. STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH This invention was made with government support under R01 ES032024, R01 HD092431, and R01 HD106112 awarded by the National Institutes of Health. The government has certain rights in the invention. REFERENCE TO SEQUENCE LISTING This application was filed with a Sequence Listing XML in ST.26 XML format accordance with 37 C.F.R. § 1.831 and PCT Rule 13ter. The Sequence Listing XML file submitted in the USPTO Patent Center, “026389-0020-WO01_sequence_listing_xml_29-JUL-2025.xml,” was created on July 29, 2025, contains 1 sequence, has a file size of 4.00 kilobytes (4,096 bytes) and is incorporated by reference in its entirety into the specification. INTRODUCTION In humans, fathers of advanced age are on the rise. Advanced paternal age not only compromises male fertility, but also poses risks to offspring health, is associated with increased risks of stillbirth, and a spectrum of complications in subsequent generations including elevated susceptibility to developmental, neuropsychiatric, and behavioral anomalies. Beyond epidemiological evidence, rodent studies further support that paternal aging can cause increased risks of metabolic disorders, obesity, and anxiety-like behaviors. While the focus of previous sperm aging research has largely been on DNA integrity and methylation patterns, recent studies have identified mammalian sperm RNAs as critical carriers of epigenetic information and as indicators of sperm quality. Emerging mouse studies have shown that zygotic injection of sperm RNAs from environmentally exposed (e.g., unhealthy diet, mentally stressed) or aged males, especially the sperm small RNA fraction that are enriched by tRNA-derived small RNAs (tsRNAs) and rRNA- derived small RNAs (rsRNAs), can induce metabolic or behavior phenotypes in mouse offspring. The direct effect of sperm RNAs in inducing offspring phenotypes, coupled with their diverse composition (e.g., tsRNAs, rsRNAs, miRNAs, and large RNAs) and sensitivity to various paternal conditions and environmental stimuli, has given rise to the concept of the “sperm RNA code,” which refers to the specific combinations of sperm RNA expression and modification signatures that convey information indicative of sperm quality and is crucial in regulating offspring health. To decode the sperm RNA code, the inventors have recently developed PANDORA-seq, an advanced sequencing method that overcomes the limitations of traditional protocols in detecting small non-coding RNAs (sncRNAs). Traditional methods for detecting and measuring RNAs often miss highly modified small RNAs like tsRNAs and rsRNAs, which originate from RNA modification-enriched parental RNAs (i.e., tRNAs and rRNAs) and have unique termini that impede their detection in standard sncRNA sequencing protocols. PANDORA-seq addresses these termini and RNA modifications with step-wise enzymatic treatments, allowing for comprehensive analyses of these previously “invisible” sncRNA species. PANDORA-seq has updated the understanding of the sncRNA landscape in sperm, for example, revealing that miRNAs constitute less than 1% of sperm sncRNAs, while tsRNAs and rsRNAs emerge as the dominant sperm sncRNAs that play critical roles in sperm-mediated epigenetic inheritance. However, methods to determine sperm quality have not been determined. Sperm aging impacts not only male fertility but also offspring’s health, yet reliable biomarkers for sperm aging remain unavailable. Thus, there is a need for methods for determining the quality of sperm in a sperm sample and performing an in vitro fertilization or artificial insemination procedure. SUMMARY The disclosure provides for other aspects and embodiments that will be apparent in light of the following detailed description and accompanying figures. One embodiment described herein is a method for determining the quality of sperm in a sperm sample comprising: (i) extracting RNA from the sperm; (ii) obtaining an RNA expression profile of the sperm using PANoramic RNA Display by Overcoming RNA modification Aborted sequencing (PANDORA-seq); (iii) predicting the quality of the sperm using a statistical prediction algorithm generated from a set of RNA expression profiles obtained from sperm in a plurality of sperm samples from a plurality of subjects of known and varying ages, wherein the set of RNA expression profiles were generated using PANDORA-seq. In one aspect, the method further comprises incubating the sperm in a sperm head lysis buffer to remove the tail and the membrane from the heads of the sperm prior to extracting the RNA from the sperm. In another aspect, the method further comprises filtering the sperm through a 40 µm cell strainer prior to incubating the sperm in the sperm head lysis buffer. In another aspect, the method further comprises treating the extracted RNA with ALKB and T4 PNK enzymes prior to generating the RNA expression profiles. In another aspect, the extracted RNA includes tsRNAs, rsRNAs, ysRNAs, snsRNAs, snosRNAs, or combinations thereof. In another aspect, the statistical prediction algorithm comprises a boxplot that plots the biological age of the plurality of subjects as a function of a variable aging score that statistically correlates the expression levels of tsRNAs and rsRNAs as a function of the ages of the plurality of subjects. In another aspect, predicting the quality of the sperm comprises calculating an aging score for the sperm based on the RNA expression profile of the sperm. In another aspect, the method further comprises generating a transformed statistical prediction algorithm from a modified set of RNA expression profiles. In another aspect, the statistical prediction algorithm continuously transforms as the set of RNA expression profiles continuously transforms as input data is received by one or more statistical or machine learning models. In another aspect, the method further comprises biochemically treating the sperm sample in a manner that manipulates RNA expression of the sperm in the sperm sample. Another embodiment described herein is a method for obtaining an RNA expression profile of sperm in a sperm sample comprising: (i) incubating the sperm in a sperm head lysis buffer to remove the membrane from the heads of the sperm; (ii) extracting RNA from the de-membraned heads of the sperm; and (iii) obtaining an RNA expression profile of the sperm. In one aspect, the method further comprises filtering the sperm through a 40 µm cell strainer prior to incubating the sperm in the sperm head lysis buffer. In another aspect, the method further comprises treating the extracted RNA with ALKB and T4 PNK enzymes prior to obtaining the RNA expression profile. In another aspect, obtaining the RNA expression profile comprises using PANoramic RNA Display by Overcoming RNA modification Aborted sequencing (PANDORA-seq). Another embodiment described herein is a method for performing an in vitro fertilization or artificial insemination procedure comprising: obtaining a plurality of sperm samples from a plurality of subjects; assessing the quality of sperm in each sperm sample according to the methods described herein; performing the in vitro fertilization or artificial insemination procedure with a sperm sample that has been determined to have high quality or that has been biochemically treated in a manner that manipulates the RNA expression of the sperm in the sperm sample. BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1A–1F show the discovery of an aging cliff during mouse sperm aging using PANDORA-seq. FIG. 1A is a diagram showing sperm collection at 5 time points (10-, 30-, 50-, 70-, and 90-weeks) during mouse aging. Cauda epididymal sperm were isolated and processed for RNA extraction, followed by PANDORA-seq or traditional sncRNA-seq. FIG.1B is a scatter plot of principal coordinate analysis (PCoA) of mouse sperm sncRNA data showing that PANDORA-seq identified a clear aging cliff during the 50–70-week transition in mouse sperm and marked a dramatic shift in tsRNA / rsRNA composition. Axis 1: the 1st principal coordinate; Axis 2: the 2nd principal coordinate. FIG. 1C is a scatter plot of PCoA of mouse sperm sncRNA data showing that traditional sncRNA-seq does not identify an aging cliff. Axis 1: the 1st principal coordinate; Axis 2: the 2nd principal coordinate. FIG. 1D, FIG. 1E, and FIG. 1F are graphs showing tsRNA expression changes during the 50-70-week transition by PANDORA-seq. The reads mapping from the PANDORA-seq shows the location from which the tsRNAs (FIG.1D and FIG.1E) and rsRNAs (FIG.1F) are derived from mature tRNAs and rRNA, respectively. The three shaded areas on the tsRNA mapping shows the location of D-loop, anticodon-loop, and T-loop (left to right) of respective tRNAs (FIG.1D and FIG.1E). The solid curves indicate the mean of RPM, while the color bands indicate the standard error of the mean. FIG.2A–2D show that sperm miRNA profiles detected by PANDORA-seq reveal an aging cliff during mouse aging, but the aging cliff is less prominent than tsRNA / rsRNA profiles detected by PANDORA-seq. FIG.2A is a diagram showing sperm collection at 5 time points (10-, 30-, 50- , 70-, and 90-weeks) during mouse aging. Cauda epididymal sperm were isolated and processed for RNA extraction, followed by PANDORA-seq or traditional sncRNA-seq. FIG.2B is a scatter plot of PCoA of mouse sperm miRNA profiles showing that PANDORA-seq identified an aging cliff during the 50–70-week transition in mouse sperm. Axis 1: the 1st principal coordinate; Axis 2: the 2nd principal coordinate. FIG. 2C is a scatter plot of PCoA of mouse sperm miRNA profiles showing that traditional sncRNA-seq does not identify an aging cliff. Axis 1: the 1st principal coordinate; Axis 2: the 2nd principal coordinate. FIG.2D is a box and whisker plot showing that the ratio of between-group variance to within-group variance (F-statistic) is significantly higher in the tsRNA / rsRNA group than the miRNA group in the PANDORA-seq data. This supports that the tsRNA / rsRNA profile shows a better separation power than the miRNA profile between the demarcated stages (early 10- / 30- / 50-weeks vs. later 70- / 90-weeks). FIG.3A–3D show that the 40-RNA signature of tsRNAs / rsRNAs associated with aging in mouse sperm heads (the sperm tsRNA and rsRNA (STAR) aging signature) provides a predictive value for human sperm aging. FIG.3A is a diagram showing the estimated life history stages in C57BL / 6J mice in comparison to human beings, and the experimental time points for sperm heads collected during mouse aging (weeks 10-90), and human longitudinal samples from 8 donors at different ages. The dashed lines illustrate the aging cliff period (50-70 weeks transition) found in mice sperm. FIG.3B is a box and whisker plot showing aging scores based on the 40-sncRNA- composed STAR aging signature, revealing the dynamic changes during mouse sperm aging and recapitulating the aging cliff during the 50-70 weeks transition. FIG.3C is a scatter plot showing the relationship between age and aging score of human sperm heads using the STAR aging signature of mice. Circles with the same color are from the same individuals. FIG.3D is a line graph showing a paired comparison of aging score for each human individual with sperm collected at T1 (an earlier time point) and T2 (a later time point). Notably, an exception was observed in one donor whose aging score decreased. Closer examination revealed that both time points of the donor’s samples, taken at ages 52 (T1) and 68 (T2), fell post-aging cliff, which was consistent with the score-plateau observed in mice after 70 weeks. FIG.4A–4F show that PANDORA-seq, but not traditional sncRNA-seq, reveals an aging cliff from samples of mouse sperm heads during mouse aging from their sncRNA profile. FIG.4A is a diagram showing collection of mouse sperm heads at 5 time points (10-, 30-, 50-, 70-, and 90-weeks) during mouse aging. The cauda epididymal sperm heads were isolated and processed for RNA extraction, followed by PANDORA-seq or traditional sncRNA-seq. FIG.4B is a scatter plot of PCoA of mouse sperm tsRNA / rsRNA profiles showing that PANDORA-seq identified an aging cliff during the 50–70-week transition in mouse sperm. Axis 1: the 1st principal coordinate; Axis 2: the 2nd principal coordinate. FIG. 4C is a scatter plot of PCoA of mouse sperm tsRNA / rsRNA profiles showing that traditional sncRNA-seq does not identify an aging cliff. Axis 1: the 1st principal coordinate; Axis 2: the 2nd principal coordinate. FIG. 4D is a scatter plot of PCoA of mouse sperm miRNA profiles showing that PANDORA-seq identified an aging cliff during the 50–70-week transition in mouse sperm. Axis 1: the 1st principal coordinate; Axis 2: the 2nd principal coordinate. FIG.4E is a scatter plot of PCoA of mouse sperm miRNA profiles showing that traditional sncRNA-seq does not identify an aging cliff. Axis 1: the 1st principal coordinate; Axis 2: the 2nd principal coordinate. FIG.4F is a box and whisker plot showing that the ratio of between-group variance to within-group variance (F-statistic) is significantly higher in the tsRNA / rsRNA group than the miRNA group when PANDORA-seq data is used. This supports that the tsRNA / rsRNA profile shows a better separation power than the miRNA profile between the demarcated stages (early 10- / 30- / 50-weeks vs. later 70- / 90-weeks) using mouse sperm heads. FIG.5A–5B show Donors with semen collections with short-term intervals shows similar aging scores between the two collections based on STAR aging signature. FIG.5A is scatter plot showing the aging scores of two individuals where semen samples were collected twice from each individual within a short time interval (i.e., two collections within a month). The grey band was derived from FIG.3C. The statistical significance was calculated along with data shown in FIG.3C and was based on a resampling test. FIG.5B is a graph showing the distribution of the resampling test. The resampling pool contained 1,000,000 random numerical scores, which were generated from a normal distribution with the same mean and standard deviation of the aging score from FIG.3C. Firstly, two scores were randomly picked up from the resampling pool and the difference between the two values (Diff1) was recorded (step 1). Next, the same procedure was repeated one more time and the difference was recorded as Diff2 (step 2). To empirically address whether the observed difference in aging score of the samples with the same age was by chance, the above resampling test (step 1 followed by step 2) was performed 100,000 times and the distribution of |Diff1| + |Diff2| was plotted. The dashed line shows the |Diff1| + |Diff2| of the real samples. The probability that the random |Diff1| + |Diff2| is less than the real |Diff1| + |Diff2| was P = 0.006, which suggests that the difference in aging score of the real samples would not arise by chance. FIG.6A–6C show that a PANDORA-seq generated, miRNA-based signature from mouse sperm aging was not predictive of human sperm aging. FIG.6A is a box and whisker plot showing aging scores with dynamic changes during mouse sperm aging that were based on a miRNA signature using PANDORA-seq. FIG.6B is a scatter plot showing the relationship between age and aging score in human sperm heads using the miRNA-based mouse aging signature, where points of the same color are from the same individual. FIG.6C is a line graph showing a paired comparison between aging score for each human individual with sperm collected at T1 (earlier time point) and T2 (later time point). FIG. 7A–7C show that a traditional sncRNA-seq generated, tsRNA / rsRNA-based signature from mouse sperm aging was not predictive of human sperm aging. FIG.7A is a box and whisker plot showing aging scores with dynamic changes during mouse sperm aging based on a tsRNA / rsRNA based signature using traditional sncRNA-seq. FIG. 7B is a scatter plot showing the relationship between age and aging score in human sperm heads using the tsRNA / rsRNA-based mouse aging signature, where points of the same color are from the same individual. FIG.7C is a line graph showing a paired comparison between aging score for each human individual with sperm collected at T1 (earlier time point) and T2 (later time point). FIG. 8A–8B show schematic representations of an overview of the PANDORA-seq protocol. FIG.8A is a schematic representation of RNA preparation and library construction steps. FIG.8B is a schematic representation of data analysis pipeline of small RNA analysis. FIG.9A–9B show SDS-PAGE analysis and quantification of purified AlkB. FIG.9A is an image of a gel showing SDS-PAGE analysis of the molecular weight and purity of AlkB protein with BSA standards using Coomassie Brilliant Blue staining. M, protein marker. FIG. 9B is a graph showing Grayscale analysis to establish a BSA standard curve and calculation of AlkB concentration. FIG. 10 is a graph showing the demethylation activity of purified AlkB protein. Relative RNA modification level of m1A, m3C, m1G and m2G with or without AlkB treatment of mouse liver total RNAs, as revealed by LC-MS / MS analysis (n = 4 biologically independent samples). The data represent means ± s.e.m. Statistical significance was determined by two-sided multiple t- test (****P < 0.0001). FIG.11A–11B show RNA size selection from Mouse sperm / liver total RNA via PAGE gel electrophoresis. FIG.11A is an image of a gel showing mouse sperm total RNAs and isolated 15-50 nt sncRNAs. FIG.11B is an image of a gel showing mouse liver total RNAs and isolated 15-50 nt sncRNAs. FIG. 12A–12D show an evaluation of mouse sperm and liver PANDORA-seq DNA libraries. FIG.12A is an image of a PAGE gel before DNA size selection for mouse sperm. The region outlined by a dashed box indicates the area excised from the gel for purification. FIG.12B is an image of a PAGE gel before DNA size selection for mouse liver. The region outlined by a dashed box indicates the area excised from the gel for purification. FIG.12C is a graph showing Bioanalyzer 2100 analysis after DNA size selection for mouse sperm. FIG. 12D is a graph showing Bioanalyzer 2100 analysis after DNA size selection for mouse liver. DETAILED DESCRIPTION Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. For example, any nomenclatures used in connection with, and techniques of biochemistry, molecular biology, immunology, microbiology, genetics, cell and tissue culture, and protein and nucleic acid chemistry described herein are well known and commonly used in the art. In case of conflict, the present disclosure, including definitions, will control. Exemplary methods and materials are described below, although methods and materials similar or equivalent to those described herein can be used in practice or testing of the embodiments and aspects described herein. As used herein, the terms “amino acid,” “nucleotide,” “polynucleotide,” “vector,” “polypeptide,” and “protein” have their common meanings as would be understood by a biochemist of ordinary skill in the art. Standard single letter nucleotides (A, C, G, T, U) and standard single letter amino acids (A, C, D, E, F, G, H, I, K, L, M, N, P, Q, R, S, T, V, W, or Y) are used herein. As used herein, terms such as “include,” “including,” “contain,” “containing,” “having,” and the like mean “comprising.” The present disclosure also contemplates other embodiments “comprising,” “consisting essentially of,” and “consisting of” the embodiments or elements presented herein, whether explicitly set forth or not. As used herein, “comprising,” is an “open- ended” term that does not exclude additional, unrecited elements or method steps. As used herein, “consisting essentially of” limits the scope of a claim to the specified materials or steps and those that do not materially affect the basic and novel characteristics of the claimed invention. As used herein, “consisting of” excludes any element, step, or ingredient not specified in the claim. As used herein, the term “a,” “an,” “the” and similar terms used in the context of the disclosure (especially in the context of the claims) are to be construed to cover both the singular and plural unless otherwise indicated herein or clearly contradicted by the context. In addition, “a,” “an,” or “the” means “one or more” unless otherwise specified. As used herein, the term “or” can be conjunctive or disjunctive. As used herein, the term “and / or” refers to both the conjunctive and disjunctive. As used herein, the term “substantially” means to a great or significant extent, but not completely. As used herein, the term “about” or “approximately” as applied to one or more values of interest, refers to a value that is similar to a stated reference value, or within an acceptable error range for the particular value as determined by one of ordinary skill in the art, which will depend in part on how the value is measured or determined, such as the limitations of the measurement system. In one aspect, the term “about” refers to any values, including both integers and fractional components that are within a variation of up to ± 10% of the value modified by the term “about.” Alternatively, “about” can mean within 3 or more standard deviations, per the practice in the art. Alternatively, such as with respect to biological systems or processes, the term “about” can mean within an order of magnitude, in some embodiments within 5-fold, and in some embodiments within 2-fold, of a value. As used herein, the symbol “~” means “about” or “approximately.” All ranges disclosed herein include both points as discrete values as well as all integers and fractions specified within the range. For example, a range of 0.1–2.0 includes 0.1, 0.2, 0.3, 0.4 . . . 2.0. If the end points are modified by the term “about,” the range specified is expanded by a variation of up to ±10% of any value within the range or within 3 or more standard deviations, including the end points, or as described above in the definition of “about.” As used herein, the terms “room temperature,” “RT,” or “ambient temperature” refer to the typical temperature in an indoor laboratory setting. In one aspect, the laboratory setting is climate controlled to maintain the temperature at a substantially uniform temperature or with a specific range of temperatures. In one aspect, “room temperature” refers a temperature of about 15–30 °C, including all integers and endpoints within the specified range. In another aspect, “room temperature” refers a temperature of about 15–30 °C; about 20–30 °C; about 22–30 °C; about 25–30 °C; about 27–30 °C; about 15–22 °C; about 15–25 °C; about 15–27 °C; about 20–22 °C; about 20–25 °C; about 20–27 °C; about 22–25 °C; about 22–27 °C; about 25–27 °C; about 15 °C ± 10%; about 20 °C ± 10%; about 22 °C ± 10%; about 25 °C ± 10%; about 27 °C ± 10%; ~20 °C, ~22 °C, ~25 °C, or ~27 °C, at standard atmospheric pressure. As used herein, the term “dose” denotes any form of an active ingredient formulation or composition, including cells, that contains an amount sufficient to initiate or produce a therapeutic effect with at least one or more administrations. “Formulation” and “composition” are used interchangeably herein. As used herein, the term “prophylaxis” refers to preventing or reducing the progression of a disorder, either to a statistically significant degree or to a degree detectable by a person of ordinary skill in the art. As used herein, the terms “effective amount” or “therapeutically effective amount,” refers to a substantially non-toxic, but sufficient amount of an action, agent, composition, or cell(s) being administered to a subject that will prevent, treat, or ameliorate to some extent one or more of the symptoms of the disease or condition being experienced or that the subject is susceptible to contracting. The result can be the reduction or alleviation of the signs, symptoms, or causes of a disease, or any other desired alteration of a biological system. An effective amount may be based on factors individual to each subject, including, but not limited to, the subject’s age, size, type or extent of disease, stage of the disease, route of administration, the type or extent of supplemental therapy used, ongoing disease process, and type of treatment desired. As used herein, the term “subject” refers to an animal. Typically, the subject is a mammal. A subject also refers to primates (e.g., humans, male or female; infant, adolescent, or adult), non- human primates, rats, mice, rabbits, pigs, cows, sheep, goats, horses, dogs, cats, fish, birds, and the like. In one embodiment, the subject is a primate. In one embodiment, the subject is a human. As used herein, a subject is “in need of treatment” if such subject would benefit biologically, medically, or in quality of life from such treatment. A subject in need of treatment does not necessarily present symptoms, particular in the case of preventative or prophylaxis treatments. As used herein, the terms “inhibit,” “inhibition,” or “inhibiting” refer to the reduction or suppression of a given biological process, condition, symptom, disorder, or disease, or a significant decrease in the baseline activity of a biological activity or process. As used herein, “treatment” or “treating” refers to prophylaxis of, preventing, suppressing, repressing, reversing, alleviating, ameliorating, or inhibiting the progress of biological process including a disorder or disease, or completely eliminating a disease. A treatment may be either performed in an acute or chronic way. The term “treatment” also refers to reducing the severity of a disease or symptoms associated with such disease prior to affliction with the disease. “Repressing” or “ameliorating” a disease, disorder, or the symptoms thereof involves administering a cell, composition, or compound described herein to a subject after clinical appearance of such disease, disorder, or its symptoms. “Prophylaxis of” or “preventing” a disease, disorder, or the symptoms thereof involves administering a cell, composition, or compound described herein to a subject prior to onset of the disease, disorder, or the symptoms thereof. “Suppressing” a disease or disorder involves administering a cell, composition, or compound described herein to a subject after induction of the disease or disorder thereof but before its clinical appearance or symptoms thereof have manifest. The terms “control,” “reference level,” and “reference” are used herein interchangeably. The reference level may be a predetermined value or range, which is employed as a benchmark against which to assess the measured result. “Control group” as used herein refers to a group of control subjects. The predetermined level may be a cutoff value from a control group. The predetermined level may be an average from a control group. Cutoff values (or predetermined cutoff values) may be determined by Adaptive Index Model (AIM) methodology. Cutoff values (or predetermined cutoff values) may be determined by a receiver operating curve (ROC) analysis from biological samples of the patient group. ROC analysis, as generally known in the biological arts, is a determination of the ability of a test to discriminate one condition from another, e.g., to determine the performance of each marker in identifying a patient having CRC. A description of ROC analysis is provided in P.J. Heagerty et al. (Biometrics 2000, 56, 337-44), the disclosure of which is hereby incorporated by reference in its entirety. Alternatively, cutoff values may be determined by a quartile analysis of biological samples of a patient group. For example, a cutoff value may be determined by selecting a value that corresponds to any value in the 25th-75th percentile range, preferably a value that corresponds to the 25th percentile, the 50th percentile or the 75th percentile, and more preferably the 75th percentile. Such statistical analyses may be performed using any method known in the art and can be implemented through any number of commercially available software packages (e.g., from Analyse-it Software Ltd., Leeds, UK; StataCorp LP, College Station, TX; SAS Institute Inc., Cary, NC.). The healthy or normal levels or ranges for a target or for a protein activity may be defined in accordance with standard practice. A control may be a subject, or a sample therefrom, whose disease state is known. The subject, or sample therefrom, may be healthy, diseased, diseased prior to treatment, diseased during treatment, or diseased after treatment, or a combination thereof. “Healthy” means that the organism, tissue, or cell has the composition and activity of an organism, tissue, or cell that falls within the boundaries of wildtype expression, indicative of a non- disease state. “Healthy” may be used interchangeably with “wildtype”. As used herein, “healthy sperm” means a sperm that has the characteristics of sperm found in healthy, fertile subjects in relation to its maturity by virtue of its location in the male reproductive tract or ejaculate. As used herein, “reduced” means that the substance or activity being measured is present in a lesser amount and / or lesser activity than when compared to that of a healthy or wildtype organism. “Sample” or “test sample” as used herein can mean any sample in which the presence and / or level of a small RNA is to be detected or determined or any sample comprising a small RNA. Samples may include liquids, solutions, emulsions, or suspensions. Samples may include a medical sample. Samples may include any biological fluid or tissue, such as semen, blood, whole blood, fractions of blood such as plasma and serum, muscle, interstitial fluid, sweat, saliva, urine, tears, synovial fluid, bone marrow, cerebrospinal fluid, nasal secretions, sputum, amniotic fluid, bronchoalveolar lavage fluid, gastric lavage, emesis, fecal matter, lung tissue, peripheral blood mononuclear cells, total white blood cells, lymph node cells, spleen cells, tonsil cells, cancer cells, tumor cells, bile, digestive fluid, skin, or combinations thereof. In some embodiments, the sample comprises an aliquot. In other embodiments, the sample comprises a biological fluid. Samples can be obtained by any means known in the art. The sample can be used directly as obtained from a patient or can be pre-treated, such as by filtration, distillation, extraction, concentration, centrifugation, inactivation of interfering components, addition of reagents, and the like, to modify the character of the sample in some manner as discussed herein or otherwise as is known in the art. “Sperm sample” as used herein refers to any sample comprising sperm. The sperm sample may be obtained from a subject, such as from a mammal, such as a primate, mouse, or human. The sperm sample may be obtained from the subject’s caput epididymis, corpus epididymis, cauda epididymis, vas deferens, testis, or ejaculate. For example, when obtained from the subject’s ejaculate, the sperm sample may be referred to as a semen sample or semen. Semen includes sperm from the testicles and fluid from the prostate and other sex glands. As used herein, “sRNA” means “small RNA” and includes all classes of small RNAs, including: small interfering RNAs (siRNAs), PIWI-interacting RNAs (piRNAs), microRNAs (miRNAs), tRNA fragments (tRF), small nucleolar RNA (snoRNA), small rDNA-derived RNA (srRNA), and small nuclear RNA (U-RNA). “Small noncoding RNA” means “sncRNA” and are short RNA sequences including transfer RNA (tRNA)-derived small RNAs (tsRNAs), ribosomal RNA-derived small RNAs (rsRNAs), miRNAs, piRNAs, siRNAs, and snoRNAs. Generally, sRNAs and sncRNAs regulate transcriptional and translational processes, and are about 200 nucleotides or less in length, such as 40 nucleotides in length, or less. “Subject” and “patient” as used herein interchangeably refers to any vertebrate, including, but not limited to, a mammal that wants or is in need of the herein described compositions or methods. The subject may be a human or a non-human. The subject may be a vertebrate. The subject may be a mammal. The mammal may be a primate or a non-primate. The mammal can be a non-primate such as, for example, cow, pig, camel, llama, hedgehog, anteater, platypus, elephant, alpaca, horse, goat, rabbit, sheep, hamsters, guinea pig, cat, dog, rat, and mouse. The mammal can be a primate such as a human. The mammal can be a non-human primate such as, for example, monkey, cynomolgous monkey, rhesus monkey, chimpanzee, gorilla, orangutan, and gibbon. The subject may be of any age or stage of development, such as, for example, an adult, an adolescent, or an infant. The subject may be male. The subject may be female. In some embodiments, the subject has a specific genetic marker. The subject may be undergoing other forms of treatment. As used herein, “untreated sperm” means sperm that have not been subjected to the application or administration of a therapeutic agent as described herein. “Treated sperm” means sperm that have been subjected to the application or administration of a therapeutic agent as described herein. Untreated sperm may be treated in vivo when exposed to a therapeutic agent as described herein that is administered to a subject comprising the untreated sperm. Untreated sperm may be treated in vitro when exposed to a therapeutic agent as described herein that is applied or administered to the untreated sperm. Methods Methods for Determining the Quality of Sperm in a Sperm Sample Provided herein are methods for determining the quality of sperm in a sperm sample. The methods may include extracting RNA from the sperm; obtaining an RNA expression profile of the sperm using PANoramic RNA Display by Overcoming RNA modification Aborted sequencing (PANDORA-seq); predicting the quality of the sperm using a statistical prediction algorithm generated from a set of RNA expression profiles obtained from sperm in a plurality of sperm samples from a plurality of subjects of known and varying ages, wherein the set of RNA expression profiles were generated using PANDORA-seq. PANDORA-seq is described in Example 1 below and Shi et al., Nature Cell Biology. 2021; 23: 424-436, which is incorporated herein by reference. The main differences between Example 1 herein and Shi et al. are the steps of RNA preparation and RNA quality, and the RNA size selection procedure (e.g., obtaining a 15- 50 nucleotide RNA fraction from a PAGE gel). In general, PANDORA-seq comprises a stepwise enzymatic treatment with T4 PNK and AlkB to eliminate / convert internal and terminal RNA modifications to enable modified small RNA to be included in a cDNA library. These modified small RNAs are not detectable by using traditional small RNA library construction protocols. The variability of the PANDORA-seq procedure mostly comes from the initial RNA input, for example, a smaller amount of initial RNA input will require more PCR amplification cycles to obtain enough cDNA for sequencing. Also, the AlkB concentration being used is dependent on the initial RNA input, to reach a concentration of about 1:1 with the input RNA. These parameters allow for achievement of an optimized outcome. PANDORA-seq employs a combinatorial enzymatic treatment to remove key RNA modifications that block adapter ligation and reverse transcription. PANDORA-seq is capable of identifying modified sncRNAs such as transfer RNA-derived small RNAs (tsRNAs) and ribosomal RNA-derived small RNAs (rsRNAs) that were previously undetected by traditional RNA-seq. The modified sncRNAs have been shown to exhibit tissue-specific expression across mouse brain, liver, spleen, and sperm, as well as cell-specific expression across embryonic stem cells (ESCs) and HeLa cells. An RNA sample for use in PANDORA-seq may be extracted from a sample by methods known in the art or methods described herein. For example, the total RNA may be extracted from a sperm sample by incubating the sperm sample with TRIzol® reagent (Invitrogen) followed by incubation with chloroform and then incubation with isopropanol. See Shi et al., Nature Cell Biol. 23: 424-436 (2021). The total RNA may be further treated to isolate a fraction of the total RNA that are a specific size of interest, such as RNAs that are from about 15 nucleotides (nt) to about 50 nt in length. The fraction of RNA of a particular size may be isolated by any means known in the art. For example, the total RNA may be loaded into a gel, separated based on size, and RNAs of the size of interest may be excised and isolated from the gel (id.). The fraction of RNAs isolated from the gel may be treated with enzymes. The enzymatic treatment may include treatment with the dealkylating enzyme α-ketoglutarate-dependent hydroxylase (AlkB) or its mutant forms to demethylate RNA modifications (for example, m1G, m1A, m3C and m22G) to enable reverse transcription, and T4 polynucleotide kinase (T4PNK) to convert 3′-P or 2′3′-cP into 3′-OH and to add a 5′-terminal phosphate (5′-P), thus facilitating adapter ligation for RNA-seq of small and large RNAs. T4PNK may be added to and incubated with the fraction of RNAs first, and then AlkB may be added to and incubated with the fraction of RNAs after T4PNK incubation. Alternatively, AlkB may be added to and incubated with the fraction of RNAs first, and then T4PNK may be added to and incubated with the fraction of RNAs after AlkB incubation. The performance of these two alternatives are very similar (Spearman’s correlation; ρ = 0.995). For example, the fraction of RNAs may be incubated with T4PNK, isolated from the enzyme by an RNA extraction process described herein, incubated with AlkB, and isolated from the enzyme by an RNA extraction process as described herein. Then, the fraction of RNAs may be sequenced by methods known in the art such as by reverse transcription. Further, the RNAs may be annotated and profiled using SPORTS as described in Shi et al., Genom. Proteom. Bioinf.16: 144–151 (2018) which is incorporated herein by reference for such teachings. Sperm samples used for the methods disclosed herein may be collected from a subject in any manner known to those skilled in the art. The collected sperm samples may include healthy, unhealthy and / or defective sperm, and may include immature and / or mature sperm. In some embodiments, the sperm sample may be collected from a mammalian subject, such as a rodent, feline, canine, porcine, bovine, or primate subject. For example, the subject may be a mouse, cat, dog, pig, cow or human, among other mammalian subjects. Sperm samples for use in the disclosed methods can be retrieved from any point along the reproductive tract from the testis to the ejaculate, including the subject’s testis, epididymis (including the caput, corpus, or cauda epididymis), vas deferens, or ejaculate. For example, the sperm sample may be obtained from the subject’s caput epididymis, corpus epididymis, or cauda epididymis using microscopic or microsurgical epididymal sperm aspiration (MESA) or percutaneous epididymal sperm aspiration (PESA). The sperm sample may be obtained from the subject’s testis using a technique selected from the group consisting of needle aspiration (TESA), percutaneous or open surgical biopsy (TESE), multibiopsy TESE, microdissection TESE, site- directed TESE after fine needle aspiration mapping, and MicroTESE. Such techniques are routinely used in assisted reproduction. The sperm sample can be a donor sperm sample, such as those available from a sperm bank. The sperm sample may have more or fewer contaminants other than the sperm depending on where and how the sperm sample is obtained. Some sperm in the sperm sample may comprise a condition such as reduced levels of sncRNA, at least one aberrant sncRNA, or the absence of at least one sncRNA that is present in healthy sperm. Alternatively, there may be increased expression levels of an sncRNA compared to healthy sperm, or the presence of sncRNAs that are not usually present in healthy sperm. A decrease in a sncRNA may be one wherein such a decrease has an effect on an offspring, such as in the case of an epigenetically transmitted condition. Sperm RNA is a sensitive biomarker for assessing fertility and sperm quality. Sperm in a sperm sample therefore should be isolated from other contaminants in the sample (e.g., somatic cells) that may affect the measurement of the RNA from the sperm prior to extraction and measurement of the RNA from the sperm to ensure that the measurement accurate. As such, the sperm samples used in the methods of the present disclosure may be treated to isolate the sperm in the sperm sample from other contaminants. For example, the sperm samples may be treated using methods that reduce or eliminate somatic cell contamination including, but not limited to, somatic cell lysis protocols, density gradient centrifugation and / or swim-up procedures. Exemplary procedures for lysing somatic cells may include, but are not limited to, treatment of the sample with somatic cell lysis buffer (e.g., solutions comprising sodium dodecyl sulfate (SDS) and Triton X-100) and / or red blood cell lysis buffer (e.g., solutions comprising (NH4Cl, NaHCO3, and / or EDTA) followed by pelleting and resuspension of the sperm. It should be noted that swim-up procedures for isolating sperm from sperm cells only select for motile spermatozoa, so may be less desirable for methods where measurement of the RNA levels of the entirety of the sperm in a sperm sample are desired. In some embodiments, contaminants additionally may be removed from a sperm sample by filtering the sample through a 30–50 µm cell strainer, such as a 40 µm cell strainer. A cell strainer that is larger than 50 µm will result in more contamination of the sample, while use of a cell strainer that is less than 30 µm will result in decreased sperm yield. Filtration with a cell strainer may be performed prior to incubating the isolated sperm in a sperm head lysis buffer (see below). In some embodiments, prior to extracting RNA from the sperm, the sperm may be incubated with a sperm head lysis buffer to remove the tail and the membrane from the heads of the sperm prior to extracting the RNA from the sperm. Examples of sperm head lysis buffer include, but are not limited to Tris-HCl, EDTA, NaCl, SDS (sodium dodecyl sulfate), and Proteinase K. For example, the sperm head lysis buffer may contain about 10 mM Tris-HCl at about pH 8.0, about 10 mM EDTA, about 50 mM NaCl, about 2% (w / v) SDS, about 75 µg / mL proteinase K, and the remainder DNase / RNase-free distilled water. The buffer may be prepared fresh and used at room temperature (i.e. from about 20 °C to about 22 °C). Methods for extracting RNA from sperm are well known to those skilled in the art, and include, but are not limited to PBS (phosphate buffered saline), TRIzol® reagent, chloroform, isopropanol, ethanol, linear acrylamide, 30-50 µm cell strainer, and syringe with 27G needle. The methods described herein may further comprise treating the extracted RNA with ALKB and T4 PNK enzymes prior to generating the RNA expression profiles. From about 0.1 ng / μL to about 100 ng / μL of ALKB enzyme may be used. For example, about 4 ng / μL of ALKB enzyme may be used. The concentration of ALKB used may be about 1:1 with the RNA, according to the actual input RNA’s concentration. The extracted RNA may be treated with ALKB enzyme at from about 25 °C to about 37 °C for from about 30 minutes to about 2 hours. From about 0.1 units / μL to about 0.3 units / μL of T4 PNK enzyme may be used. For example, about 0.2 units / μL of T4 PNK enzyme may be used. The extracted RNA may be treated with T4 PNK enzyme at about 37 °C for from about 20 minutes to about 30 minutes. The extracted RNA may include tsRNAs, rsRNAs, other small non-coding RNAs, or combinations thereof. Other small non-coding RNAs may be, but are not limited to, ysRNAs, snsRNAs, snosRNAs, and the like. A tangible computer-readable medium comprising computer-readable code that, when executed by a computer, causes the computer to perform operations comprising receiving information corresponding to RNA levels of a set of RNA families in a biological sample, the RNA families may include, but are not limited to, those listed in Table 2, and determining the age of the biological sample by applying a statistical prediction algorithm to the measured RNA signature. The statistical prediction algorithm may comprise a boxplot that plots the biological age of the plurality of subjects as a function of a variable aging score that statistically correlates the expression levels of RNA, such as tsRNAs and rsRNAs, as a function of the ages of the plurality of subjects. Predicting the quality of the sperm may comprise calculating an aging score for the sperm based on the RNA expression profile of the sperm. The methods described herein may further comprise generating a transformed statistical prediction algorithm from a modified set of RNA expression profiles. The statistical prediction algorithm may continuously transform as the set of RNA expression profiles continuously transforms as input data is received by one or more statistical or machine learning models. The methods described herein may further comprise biochemically treating the sperm in a manner that manipulates RNA expression of the sperm in the sperm sample. Biochemical treatments that may be used in the methods disclosed herein include, but are not limited to T4 PNK, T4 PNK kinase reaction buffer (including, but are not limited to Tris-HCl, MgCl2, and dithiothreitol (DTT)), ALKB, HEPES-KOH buffer, ferrous ammonium sulfate, α-ketoglutarate, sodium ascorbate, bovine serum albumin (BSA), and RNase Inhibitor. Methods for Performing an In Vitro Fertilization or Artificial Insemination Procedures Provided herein are methods for performing an in vitro fertilization or artificial insemination procedure. The methods may include obtaining a plurality of sperm samples from a plurality of subjects as described herein; assessing the quality of sperm in each sperm sample according to the methods described herein; performing the in vitro fertilization or artificial insemination procedure with a sperm sample that has been determined to have high quality or that has been biochemically treated in a manner that manipulates the RNA expression of the sperm in the sperm sample. Methods for obtaining sperm samples and assessing the quality of the sperm in each sample have been described in detail above. If the quality of the sperm in the sperm sample has been determined to be inadequate, then the sperm sample either may be discarded, or may be biochemically treated in a manner that manipulates the quality of the sperm sample, as determined by a subsequent analysis showing that the RNA expression profile of the sperm in the sample is consistent with that of high-quality sperm. Biochemical treatments that may be used in the methods disclosed herein include but are not limited to phosphate-buffered saline (PBS), SDS (sodium dodecyl sulfate), and Triton® X-100. Once a sperm sample has been determined to have high quality, the sperm may be used to fertilize an egg by performing in vitro fertilization or artificial insemination. A mammalian “oocyte,” which may also be referred to as an “ovocyte,” “immature ovum,” or “egg cell” for use in the disclosed methods may be produced through oogenesis by meiotic division. An oocyte for use in in vitro fertilization can be retrieved from a subject by any known method, including aspiration directly from the ovarian follicles. An oocyte for use in in vivo fertilization is not retrieved, but fuses with the sperm within the subject prior to implantation in the uterus of a subject. An oocyte may be fertilized by any known method, including in vivo methods and in vitro methods. The method of oocyte fertilization may be in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI). One embodiment described herein is a method for determining the quality of sperm in a sperm sample comprising: (i) extracting RNA from the sperm; (ii) obtaining an RNA expression profile of the sperm using PANoramic RNA Display by Overcoming RNA modification Aborted sequencing (PANDORA-seq); (iii) predicting the quality of the sperm using a statistical prediction algorithm generated from a set of RNA expression profiles obtained from sperm in a plurality of sperm samples from a plurality of subjects of known and varying ages, wherein the set of RNA expression profiles were generated using PANDORA-seq. In one aspect, the method further comprises incubating the sperm in a sperm head lysis buffer to remove the tail and the membrane from the heads of the sperm prior to extracting the RNA from the sperm. In another aspect, the method further comprises filtering the sperm through a 40 µm cell strainer prior to incubating the sperm in the sperm head lysis buffer. In another aspect, the method further comprises treating the extracted RNA with ALKB and T4 PNK enzymes prior to generating the RNA expression profiles. In another aspect, the extracted RNA includes tsRNAs, rsRNAs, ysRNAs, snsRNAs, snosRNAs, or combinations thereof. In another aspect, the statistical prediction algorithm comprises a boxplot that plots the biological age of the plurality of subjects as a function of a variable aging score that statistically correlates the expression levels of tsRNAs and rsRNAs as a function of the ages of the plurality of subjects. In another aspect, predicting the quality of the sperm comprises calculating an aging score for the sperm based on the RNA expression profile of the sperm. In another aspect, the method further comprises generating a transformed statistical prediction algorithm from a modified set of RNA expression profiles. In another aspect, the statistical prediction algorithm continuously transforms as the set of RNA expression profiles continuously transforms as input data is received by one or more statistical or machine learning models. In another aspect, the method further comprises biochemically treating the sperm sample in a manner that manipulates RNA expression of the sperm in the sperm sample. Another embodiment described herein is a method for obtaining an RNA expression profile of sperm in a sperm sample comprising: (i) incubating the sperm in a sperm head lysis buffer to remove the membrane from the heads of the sperm; (ii) extracting RNA from the de-membraned heads of the sperm; and (iii) obtaining an RNA expression profile of the sperm. In one aspect, the method further comprises filtering the sperm through a 40 µm cell strainer prior to incubating the sperm in the sperm head lysis buffer. In another aspect, the method further comprises treating the extracted RNA with ALKB and T4 PNK enzymes prior to obtaining the RNA expression profile. In another aspect, obtaining the RNA expression profile comprises using PANoramic RNA Display by Overcoming RNA modification Aborted sequencing (PANDORA-seq). Another embodiment described herein is a method for performing an in vitro fertilization or artificial insemination procedure comprising: obtaining a plurality of sperm samples from a plurality of subjects; assessing the quality of sperm in each sperm sample according to the methods described herein; performing the in vitro fertilization or artificial insemination procedure with a sperm sample that has been determined to have high quality or that has been biochemically treated in a manner that manipulates the RNA expression of the sperm in the sperm sample. It will be apparent to one of ordinary skill in the relevant art that suitable modifications and adaptations to the compositions, formulations, methods, processes, and applications described herein can be made without departing from the scope of any embodiments or aspects thereof. The compositions and methods provided are exemplary and are not intended to limit the scope of any of the specified embodiments. All of the various embodiments, aspects, and options disclosed herein can be combined in any variations or iterations. The scope of the compositions, formulations, methods, and processes described herein include all actual or potential combinations of embodiments, aspects, options, examples, and preferences herein described. The exemplary compositions and formulations described herein may omit any component, substitute any component disclosed herein, or include any component disclosed elsewhere herein. The ratios of the mass of any component of any of the compositions or formulations disclosed herein to the mass of any other component in the formulation or to the total mass of the other components in the formulation are hereby disclosed as if they were expressly disclosed. Should the meaning of any terms in any of the patents or publications incorporated by reference conflict with the meaning of the terms used in this disclosure, the meanings of the terms or phrases in this disclosure are controlling. Furthermore, the foregoing discussion discloses and describes merely exemplary embodiments. All patents and publications cited herein are incorporated by reference herein for the specific teachings thereof. Various embodiments and aspects of the inventions described herein are summarized by the following clauses: For reasons of completeness, various aspects of the invention are set out in the following numbered clauses: Clause 1. A method for determining the quality of sperm in a sperm sample comprising: (i) extracting RNA from the sperm; (ii) obtaining an RNA expression profile of the sperm using PANoramic RNA Display by Overcoming RNA modification Aborted sequencing (PANDORA-seq); (iii) predicting the quality of the sperm using a statistical prediction algorithm generated from a set of RNA expression profiles obtained from sperm in a plurality of sperm samples from a plurality of subjects of known and varying ages, wherein the set of RNA expression profiles were generated using PANDORA-seq. Clause 2. The method of clause 1, further comprising incubating the sperm in a sperm head lysis buffer to remove the tail and the membrane from the heads of the sperm prior to extracting the RNA from the sperm. Clause 3. The method of clause 1 or 2, further comprising filtering the sperm through a 40 µm cell strainer prior to incubating the sperm in the sperm head lysis buffer. Clause 4. The method of any one of clauses 1–3, further comprising treating the extracted RNA with ALKB and T4 PNK enzymes prior to generating the RNA expression profiles. Clause 5. The method of any one of clauses 1–4, wherein the extracted RNA includes tsRNAs, rsRNAs, ysRNAs, snsRNAs, snosRNAs, or combinations thereof. Clause 6. The method of any one of clauses 1–5, wherein the statistical prediction algorithm comprises a boxplot that plots the biological age of the plurality of subjects as a function of a variable aging score that statistically correlates the expression levels of tsRNAs and rsRNAs as a function of the ages of the plurality of subjects. Clause 7. The method of any one of clauses 1–6, wherein predicting the quality of the sperm comprises calculating an aging score for the sperm based on the RNA expression profile of the sperm. Clause 8. The method of any one of clauses 1–7, wherein the method further comprises generating a transformed statistical prediction algorithm from a modified set of RNA expression profiles. Clause 9. The method of any one of clauses 1–8, wherein the statistical prediction algorithm continuously transforms as the set of RNA expression profiles continuously transforms as input data is received by one or more statistical or machine learning models. Clause 10. The method of any one of clauses 1–9, further comprising biochemically treating the sperm sample in a manner that manipulates RNA expression of the sperm in the sperm sample. Clause 11. A method for obtaining an RNA expression profile of sperm in a sperm sample comprising: (i) incubating the sperm in a sperm head lysis buffer to remove the membrane from the heads of the sperm; (ii) extracting RNA from the de-membraned heads of the sperm; and (iii) obtaining an RNA expression profile of the sperm. Clause 12. The method of claim 11, further comprising filtering the sperm through a 40 µm cell strainer prior to incubating the sperm in the sperm head lysis buffer. Clause 13. The method of claim 11 or 12, further comprising treating the extracted RNA with ALKB and T4 PNK enzymes prior to obtaining the RNA expression profile. Clause 14. The method of any one of clauses 11–13, wherein obtaining the RNA expression profile comprises using PANoramic RNA Display by Overcoming RNA modification Aborted sequencing (PANDORA-seq). Clause 15. A method for performing an in vitro fertilization or artificial insemination procedure comprising: obtaining a plurality of sperm samples from a plurality of subjects; assessing the quality of sperm in each sperm sample according to the method of any of claims 1-9; performing the in vitro fertilization or artificial insemination procedure with a sperm sample that has been determined to have high quality or that has been biochemically treated in a manner that manipulates the RNA expression of the sperm in the sperm sample. EXAMPLES The foregoing may be better understood by reference to the following examples, which are presented for purposes of illustration and are not intended to limit the scope of the invention. The present disclosure has multiple aspects and embodiments, illustrated by the appended non- limiting examples. Example 1 Methods of Measuring small RNAs using PANDORA-seq Small noncoding RNAs (sncRNAs) go beyond the traditionally studied siRNAs, miRNAs, and piRNAs, encompassing a broader range derived from structured RNAs like tRNAs, rRNAs, and others. Table 1 shows sncRNAs. Table 1. SncRNA categories according to their parental origin Parental RNA Derivative sncRNA transfer RNA (tRNA) tRNA-derived-small RNA (tsRNA) ribosomal RNA (rRNA) rRNA-derived-small RNA (rsRNA) Y RNA (yRNA) yRNA-derived-small RNA (ysRNA) Vault RNA (vtRNA) vtRNA-derived-small RNA (vtsRNA) small nuclear RNA (snRNA) snRNA-derived-small RNA (snsRNA) small nucleolar RNAs (snoRNA) snoRNA-derived-small RNA (snosRNA) long non-coding RNA (lncRNA) lncRNA-derived-small RNA (lncsRNA) messenger RNA (mRNA) mRNA-derived-small RNA (msRNA) These sncRNAs have varied termini and RNA modifications, which can cause biases in standard RNA-sequencing methods as they interfere with adaptor ligation and reverse transcription processes during cDNA library construction. To address this limitation, PANDORA- seq introduces a refined methodology. It starts with a series of enzymatic treatments with T4PNK and AlkB, which effectively circumvent the challenges presented by the ligation-blocking termini and RT-blocking RNA modifications, followed by tailored small RNA library construction protocols and deep sequencing; the obtained datasets are analyzed by SPORTS1.1 pipeline, which can comprehensively analyze various types of sncRNAs beyond the miRNA / piRNAs, including sncRNAs derived from various parental RNAs (tRNA, rRNA, snoRNA, snRNA, Y RNA, Vault RNA, etc), as well as outputting the sncRNA mapping information in regard to the location from which they are derived from the parental RNAs. PANDORA-seq provides a transformative tool to further the understanding of the expanding small RNA universe and to explore their uncharted function. PANDORA-seq, incorporates a stepwise enzymatic treatments (T4PNK + AlkB) to eliminate the obstacles presented by ligation-blocking termini and RT-blocking RNA modifications (Shi et al., Nature cell biology (2021) 23, 424-436). Subsequent to these treatments, the process includes small RNA library preparation protocols that are tailored according to RNA sample quantities to improve the success rate of library construction (FIG. 8A). PANDORA-seq has revealed the fact that miRNAs, once presumed to be the dominant species, actually comprised only a scant 0.1–5% of the total sncRNA population across diverse cells and tissues. While majority of sncRNAs are tsRNAs and rsRNAs in most tissue / cell types. The deep sequencing datasets are then processed by the SPORTS1.1 pipeline, Shi et al., Genomics Proteomics Bioinformatics 16: 144-151 (2018), which can systematically analyze various types of sncRNAs beyond the miRNA / piRNAs, including sncRNAs derived from various parental RNAs, as well as outputting the sncRNA (e.g., tsRNA, rsRNA, ysRNA, snsRNA, snosRNA, vtsRNA) mapping information in regard to the location from which they are derived from the parental RNAs (FIG. 8B). The mapping results can facilitate the direct visual comparison between samples (e.g., different tissues / cells, or different experimental groups) regarding the location and quantity of sncRNAs of different origins. The protocols were used to demonstrate that low-sample input as low as 1 ng of small RNAs allowed for detection of the RNAs, which suggests that a wide-range of scarce samples may be detectable with these methods. While the protocols are written in present tense, these methods were performed and used to generate the data and results shown herein. Preparation and Validation of AlkB Enzyme Activity The AlkB enzyme is crucial for PANDORA-seq, designed to remove several RNA modifications that impede the full-length reverse transcription efficiency of sncRNA during cDNA library preparation. The purity and activity of the AlkB enzyme significantly influences the demethylation efficiency and can lead to RNA degradation during reaction steps, potentially resulting in the failure of library construction or the incorporation of excessively short fragmented sncRNA in the sequencing data. Thus, the AlkB enzyme should be prepared by an artisan with expertise in protein expression and purification to ensure high-quality outcomes. The AlkB gene used in this protocol features sequence variations at the amino terminus compared to previously documented AlkB sequences (Table 2; Shi et al., Nature Cell Biol. 23: 424-436 (2021)). These modifications enhance the enzyme’s demethylation activity on m2G, in addition to its established activity on m1A, m3C, and m1G. The demethylation activity of the purified AlkB protein must be confirmed through LC-MS / MS (steps 24 to 50). Typically, 500 mL of cell culture yields protein at the milligram level, sufficient for subsequent experiments. In the demethylation system, the enzyme to RNA ratio is maintained at 1:1. Table 2. AlkB gene sequence used for protein expression (5′→3′) AlkB gene sequence (SEQ ID NO: 1) CTGGACCTGTTCGCGGATGCGGAGCCGTGGCAGGAACCGCTGGCGGCGGGTGCGGTTATCCTGCGTCGTTTCGCGT TTAACGCGGCGGAGCAACTGATCCGTGACATTAACGATGTGGCGAGCCAGAGCCCGTTTCGTCAAATGGTTACCCC GGGTGGCTACACCATGAGCGTGGCGATGACCAACTGCGGTCACCTGGGTTGGACCACCCACCGTCAGGGTTACCTG TATAGCCCGATCGACCCGCAAACCAACAAGCCGTGGCCGGCGATGCCGCAGAGCTTCCACAACCTGTGCCAACGTG CGGCGACCGCGGCGGGTTACCCGGACTTTCAGCCGGATGCGTGCCTGATTAACCGTTATGCGCCGGGTGCGAAGCT GAGCCTGCACCAAGACAAAGATGAGCCGGATCTGCGTGCGCCGATCGTTAGCGTGAGCCTGGGTCTGCCGGCGATT TTCCAGTTTGGTGGCCTGAAGCGTAACGACCCGCTGAAACGTCTGCTGCTGGAGCACGGCGATGTGGTTGTGTGGG GTGGCGAAAGCCGTCTGTTCTACCACGGTATCCAGCCGCTGAAAGCGGGCTTTCACCCGCTGACCATTGACTGCCG TTATAACCTGACCTTCCGTCAAGCGGGTAAGAAAGAA Preparation of RNA Samples PANDORA-seq is adaptable to a wide range of biological samples, yet it requires careful attention to RNA integrity. RNA degradation can significantly affect the accuracy of small RNA quantification and profiling. Exceptions exist, notably in samples such as mouse sperm or sperm head, which lack the 18S and 28S ribosomal RNA peaks. To prevent degradation of precursor tRNA / rRNA during enzymatic treatment, RNAs ranging from 15 to 50 nucleotides should be isolated. The variability in the proportion of small RNAs relative to the total RNAs across different samples should be noted. Therefore, the quantification of small RNA concentration (step 62 below) is required. Moreover, after evaluating various small RNA isolation protocols, the optimized protocol is detailed from step 52 to 62 below, ensuring the most efficient recovery of small RNAs. Library preparation and Controls The RNA sample should be divided, using half to construct traditional (no modification related enzyme treated) libraries and the other half for PANDORA-seq library construction. Traditional sncRNA-seq data can be controls for conducting parallel analyses to distinguish between modified and unmodified small RNA types. To achieve statistically significant results in pairwise comparisons, it is recommended to perform PANDORA-seq with at least three biological replicates. The library preparation requires expertise in molecular biology. Caution should be taken to prevent RNase contamination, which can lead to artificial RNA fragmentation during library construction. Incorporating synthetic small RNAs as spike-ins before library preparation can serve as internal controls to standardize sncRNA expression levels. However, it should be noted that adding spike-in RNA into RNA samples with the same quantity of total RNAs will be problematic if different numbers of cells in the two groups contribute equal quantities of total RNAs. Users can also include additional customized controls based on specific needs and objectives. Quality Control and Sequencing Considerations Successful libraries should yield at least 1 nM of purified cDNAs, or meet the specific requirements of sequencing centers (steps 66–86 below). Typically, the peak length of libraries should range from approximately 130 to 170 bp, with the average length of a PANDORA-seq library being marginally longer than that of traditional small RNA libraries. Small RNA samples generally require a minimum of 10 million reads; however, sequencing additional reads can improve the accuracy of quantification. During data analysis, an initial assessment can be made by checking the annotation rate of the total clean reads, which usually exceeds 90%. This serves as a preliminary check for any sample contamination. Reagents flagged as RNase-free should be prepared under RNase-free conditions. Clean gloves and the working area with 75% ethanol and nuclease decontamination solution. Use RNase-free water as indicated. Reagents Yeast extract tryptone (YT) medium. Prepared by dissolving 31 g 2x YT powder in 1000 mL ultrapure water. Sterilized by autoclaving for 20 min, then the solution was cooled to room temperature before use. LB medium. Prepared by dissolving 1.1 g of LB broth in 48.3 mL of ultrapure water. Sterilized by autoclaving for 20 min, then the solution was cooled to room temperature before use. LB medium (containing agar). Prepared by dissolving 1.68 g of LB broth with agar in 48.3 mL of ultrapure water. Sterilized by autoclaving for 20 min. Allowed to cool slightly before adding Kanamycin to achieve a final concentration of 50 μg / mL. Poured into Petri dishes and waited for it to solidify. 50% (vol / vol) Glycerol solution. Prepared 200 mL of 50% glycerol solution by adding ultrapure water to 100 mL of glycerol to achieve a total volume of 200 mL. Sterilized by autoclaving for 20 min, then cooled the solution to room temperature before use. Prepared fresh before usage. Lysis buffer. Prepared 1 L of lysis buffer by combining 100 mL of 10x PBS, 30 mL of 5 M NaCl, and 400 µL of 5 M imidazole solution. Topped up with ultrapure water to 1 L total volume. Prepared fresh before usage. Wash buffer. Prepared 1 L of lysis buffer by combining 100 mL of 10x PBS, 30 mL of 5 M NaCl, and 1 mL of 5 M imidazole solution. Topped up with ultrapure water to 1 L total volume. Prepared fresh before usage. Elution buffer. Prepared 1 L of elution buffer by combining 20 mL of 1 M HEPES (pH 8.0), 33.3 mL of 3M KCl solution, 40 mL of 5 M imidazole solution, 1 mL of 1 M DTT solution, and 200 mL of 50% glycerol solution. Topped up with ultrapure water to 1 L total volume. Prepared fresh before usage. Storage buffer. Prepared 50 mL of storage buffer by combining 1 mL of 1 M Tris-HCl (pH 8.0), 25 mL of pure Glycerol, 2 mL of 5 M NaCl solution, and 100 µL of 1 M DTT solution. Topped up with RNase-free water to 50 mL total volume. Stored at 4°C for up to 1 month. Must be RNase-free. 50 mM NH4OAc (pH 5.3). Dissolved 0.03854 g of NH4OAc in 5 mL of RNase-free water. Adjusted pH to 5.3 with ammonia or acetic acid. Adjusted the volume to 10 mL with RNase-free water. Must be RNase-free. 10% APS (wt / vol). Dissolved 1 g of ammonium persulfate in 10 mL of RNase-free water to prepare a 10% APS solution. The solution can be stored at 4 °C for at least 3 months. Must be RNase-free. 1X TBE. Diluted 10x concentrated TBE buffer 10-fold with RNase-free water. Must be RNase-free. 1.5 mM ferrous ammonium sulfate solution. Dissolved 0.1176 g of ferrous ammonium sulfate hexahydrate in 10 mL of RNase-free water to prepare a 30 mM ferrous ammonium sulfate solution. Diluted it 20-fold with RNase-free water. Must be RNase-free. 10 mM α-Ketoglutaric acid solution. Dissolved 0.1461 g of α-Ketoglutaric acid in 10 mL of RNase-free water to prepare a 100 mM α- Ketoglutaric acid solution. Diluted it 10-fold with RNase-free water. Must be RNase-free. 20 mM sodium ascorbate solution. Dissolved 0.3962 g of sodium ascorbate in 10 mL of RNase-free water to prepare a 200 mM sodium ascorbate solution. Diluted it 10-fold with RNase- free water. Must be RNase-free. 0.5 mg / mL Bovine Serum Albumin (BSA) solution. Dissolved 0.5000 g of Bovine Serum Albumin in 10 mL of RNase-free water to prepare a 5 mg / mL BSA solution. Diluted it 10-fold with RNase-free water. Must be RNase-free. 10% (wt / vol) SDS solution. Prepared 40 mL of a 10% (wt / vol) SDS stock solution by dissolving 5 g of SDS in RNase-free water. Adjusted the final volume to 50 mL with RNase-free water. Sterilized the solution by filtering through a 0.22-μm filter. Can be stored at room temperature for up to 12 months. Must be RNase- free. Urea-PAGE gel elution buffer. Prepared 50 mL of gel elution buffer by combining 1 mL of a 1 M Tris-HCl (pH 7.5), 4.2 mL of 3 M sodium acetate, 100 µL of 0.5 M EDTA and 1.25 mL of 10% SDS solution. Adjusted the final volume to 50 mL with RNase-free water. Warmed the buffer to 37 °C if SDS precipitates. Can be stored indefinitely at room temperature. Must be RNase- free. 5 M Imidazole, pH 8.0. Dissolved 17.02 g of imidazole in 40 mL of RNase-free water. Adjusted pH to 8.0 with NaOH. After adjusting the volume to 50 mL with RNase-free water, sterilized through a 0.22-μm filter. Can be stored at room temperature for up to 12 months. Must be RNase-free. 1 M DTT solution. Dissolved 1.543 g of DTT in 8 mL of RNase-free water, topped up to 10 mL with additional RNase- free water. The solution can be divided into aliquots of 1 mL and stored at −20°C for up to 12 months. Must be RNase-free. Sperm isolation buffer. Prepared 10 mL of sperm isolation buffer by combining 150 µL of a 10% SDS solution and 750 µL of Triton X-100 solution. Topped up with ultrapure water to 10 mL total volume. Sperm head isolation buffer. Prepared 10 mL of sperm head isolation buffer by combining 100 µL of 1 M Tris-HCl (pH 8.0), 200 µL of 0.5 M EDTA, 500 µL of 1 M NaCl, 2 mL of 10% SDS solution, and 37.5 µL of Proteinase K. Added ultrapure water to reach a total volume of 10 mL. Protocol Expression and purification of AlkB protein (timing about 5 days) was done using the following steps (steps 1–23): Step 1. Thaw 40 μL of Escherichia coli BL21 (DE3) competent cells on ice for 5 min. Thaw competent cells on ice and use them immediately to avoid freeze-thaw cycles. Step 2. Transfer 1 μL of 500 ng / μL pET28a-AlkB-6xHis plasmid into 40 μL BL21(DE3) competent cells. Ensure thorough mixing by gentle agitation. The sequence for the AlkB gene is detailed in Table 2. Step 3. Incubate the transformed cells on ice for 20 min, followed by a heat shock at 42 °C for 90 s, then immediately cool on ice for 2 min. Step 4. Incubate the transformed cells in 1 mL of LB medium at 37 °C with shaking for 30 min. Centrifuge the cells at 1000 g for 3 min, then resuspend them in 100 µL of LB medium, and spread on an LB agar plate containing 50 µg / mL kanamycin. Step 5. Incubate the plate at 37°C for 14–16 h, or until colonies have grown to the desired size. Isolate a single colony and inoculate it into 3 mL of YT medium supplemented with 50 µg / mL kanamycin. Incubate the culture at 37 °C with orbital shaking at 220 rpm for 12 h. Step 6. Dilute an appropriate volume of culture at a 1:20 ratio into 3 mL of YT medium supplemented with 50 µg / mL kanamycin. Continue incubation at 37 °C with 220 rpm shaking. Monitor its absorbance until OD600value between 0.5–0.6. Preserve cells containing the AlkB plasmid for long-term storage by combining the cell culture with an equal volume of 50% glycerol. Store the glycerol stock at −80 °C for up to one year, minimizing freeze-thaw cycles. Step 7. Scrape some of the frozen glycerol stock of cells that transformed with the pET28a-AlkB- 6xHis plasmid using a 10 μL pipette tip. Do not thaw the glycerol stock. Step 8. Incubate the cells in 500 mL YT medium containing 50 μg / mL kanamycin at 37 °C with 220 rpm shaking, monitor its absorbance until OD600value reaches 0.5-0.6. Step 9. Add IPTG to achieve a final concentration of 0.5 mM in the culture, and then reduce the temperature to 16°C. Incubate the cells overnight with 220 rpm shaking. Step 10. Centrifuge the cells at 6,000 × g at 4 °C for 5 min to collect the cells. Step 11. Resuspend the cell pellet in 20 mL pre-cooled lysis buffer. Step 12. Homogenize the cells on ice using a 20 mL syringe, ensuring complete resuspension to prevent clogging in the cell disruptor. (Optional) Add 10 mg / mL DNase I to avoid cell clogging. Keep the cell lysate on ice to maintain the enzyme activity of AlkB protein. Step 13. Configure the cell disruptor to operate at a temperature of 4 °C and a pressure setting of 1200 bar. Sequentially rinse the sample column with 75% ethanol, ultrapure water, and pre-cooled lysis buffer. Recycle the cell suspension through the disruptor 6 to 8 times, ensuring to collect the lysate on ice throughout the process. Step 14. Centrifuge the disrupted lysate at 13,000 × g at 4 °C for 40 min. Transfer the supernatant to a new 50 mL centrifuge tube. Step 15. Add 1 mL of the Ni-NTA resin to a fresh tube. Proceed to wash the resin by adding 5 mL of pre-cooled lysis buffer. Centrifuge the mixture at 1,000 × g for 30 s at 4 °C, then discard the supernatant. Repeat this washing procedure 2-3 times. Step 16. Add 20 mL of cell lysate to the tube and mix thoroughly. Incubate the mixture on a roller mixer at 4°C for 2 h. Step 17. Prepare an affinity chromatography column by washing it with 2–3 column volumes of the p re-cooled lysis buffer. Step 18. Transfer the mixture from step 16 to the column. allow the mixture to flow through the column by gravity at 4 °C. Step 19. Wash the column with 5–6 column volumes of pre-cooled wash buffer. Step 20. Elute the bound proteins with pre-cooled elution buffer, allowing each 1 mL addition to sit for 3–5 min before collect the flow-through. Repeat the elution process until no further protein is detected by Coomassie Brilliant Blue staining in the flow-through. Step 21. Transfer the flow-through to a concentration column with a 10 kDa molecular weight cut-off. Centrifuge at 6,000 × g at 4 °C for 10 min. Discard the waste and replace with an equal volume of storage buffer. Repeat the centrifugation and buffer replacement steps 5 to 6 times to completely substitute the elution buffer with storage buffer. Continue centrifugation at 6,000 g at 4°C until no additional fluid collects at the bottom. Step 22. Subject the collected protein to 12% SDS-PAGE electrophoresis followed by Coomassie Brilliant Blue staining to evaluate the purity and concentration of AlkB protein (FIGS. 9A-B). Dilute the protein concentration to 200 ng / μL with storage buffer. Step 23. Aliquot the diluted AlkB protein solution as needed, flash-freeze in liquid nitrogen, and store at -80°C for long-term storage. To prevent enzyme inactivation due to repeated freeze- thaw cycles, dilute the protein to a working concentration of 20 ng / μL using storage buffer and store at −20 °C for up to one month. Demethylation activity validation of purified AlkB protein by LC-MS / MS (timing 1 day) was done using the following steps (steps 24–50) and steps 25–42 were performed under RNase-free conditions. The gloves and working area were cleaned with 75% ethanol and Nuclease Decontamination Solution. RNase-free water was used when indicated. Step 24. Isolate 50–100 mg of liver sample per 1.5 mL microcentrifuge tube from a C57BL / 6J mouse. Step 25. Add 1 mL of TRIzol Reagent to each tube and homogenize thoroughly. Let the mixture stand at room temperature for 5 min. Step 26. Add 0.2 mL of chloroform per 1 mL of TRIzol Reagent used. Securely cap the tubes, shake vigorously for 15 s, then let stand at room temperature for 10–15 min. Step 27. Centrifuge the tubes at 13,000 × g for 15 min at 4 °C. Step 28. Carefully transfer the aqueous phase to a fresh tube. Add 0.5 mL isopropanol per 1 mL of TRIzol Reagent used to the aqueous phase and then mix gently. Optionally, add 1 µL of linear acrylamide per tube to aid in RNA precipitation. Step 29. Let the mixture stand at room temperature for 10 min and then centrifuge at 13,000 × g for 10 min at 4 °C. Step 30. Carefully remove and discard the supernatant. Add 1 mL of 75% ethanol per 1 mL of TRIzol Reagent used. The RNA precipitate can be stored in 75% ethanol at −80 °C at least one year. Step 31. Briefly vortex the tube, then centrifuge at 7,500 × g for 5 minutes at 4 °C. Carefully discard the supernatant. Air dry the RNA pellet for 5–10 min. Step 32. Resuspend the RNA pellet in 100 µL of RNase-free water by pipetting up and down several times until the RNA pellet is completely dissolved. The RNA solution can be used immediately or stored at −80 °C. Step 33. Evaluate and quantify the quality and concentration of RNA using the Qubit fluorometer and the Bioanalyzer 2100 with the RNA nano 6000 kit according to the manufacturer’s instructions. Step 34. Measure and dilute 200 ng of mouse liver total RNAs in 10 µL of RNase-free water in preparation for AlkB treatment. Step 35. Prepare the AlkB enzyme treatment by mixing the components specified in Table 3. Adjust the volumes for each reaction based on the guidelines provided, including an extra 10% volume to account for pipetting losses. Incubate the reaction mixture at 37 °C for 30 min. Table 3. Reaction Components Component Volume Final Concentration (50 µL reaction) Input RNA 10 μL 1 M HEPES 2.5 μL 50 mM 1.5 mM ferrous ammonium sulfate 2.5 µL 75 μM 10 mM 10× α-Ketoglutaric acid 5 µL 1 mM 20 mM sodium ascorbate 5 µL 2 mM 0.5 mg / mL BSA 5 µL 2.5 μg RNase Inhibitor 2.5 µL 100 units AlkB (20 ng / μL) 10 µL 4 ng / μL H2O 7.5 µL Total 50 μL Step 36. Purify RNAs from the mixture following steps 25-31. Then Dissolve 200 ng of AlkB-treated liver total RNAs in 50 μL of 50 mM NH4OAc. Step 37. Dissolve 200 ng of AlkB-untreated liver total RNAs in 50 μL of 50 mM NH4OAc. Step 38. Add 0.2 U of nuclease P1 to the RNA mixture and incubate at 50 °C for 3 h. Step 39. Add 0.04 U of phosphodiesterase I to the RNA mixture and incubate at 37 °C for 2 h. Step 40. Add 2 U of alkaline phosphatase to the RNA mixture and incubate at 37 °C for 2 h. Alternatively, replace steps 37-40 with a one-step digestion system. Digest 200 ng of RNA from mouse liver tissue in a 50 µL reaction buffer containing 250 mM Tris-HCl (pH 8.0), 5 mM MgCl2, 500 ng / mL BSA, 1 U Benzonase nuclease, 0.2 IU alkaline phosphatase, and 0.05 IU phosphodiesterase I at 37°C for 3 h. Step 41. Transfer the mixture into Nanosep centrifugal devices with 3K Omega membrane and centrifuge at 10,000 × g at 4 °C for 20 min. Step 42. Transfer the aqueous phase containing mononucleosides to a fresh tube, use immediately for LC-MS / MS analysis or store at −80 °C. The mononucleoside sample can be stored at −80°C for at least 6 months. Step 43. Use an Acquity UPLC I-class chromatography system with an Acquity UPLC HSS T3 1.8 μm 2.1 × 150 mm column at 40 °C for chromatographic separation of the mononucleoside sample. Set up the flow rate at 0.3 mL / min. Step 44. Prepare mobile phase A with 0.1% formic acid in distilled water and mobile phase B with 0.1% formic acid in acetonitrile (HPLC grade, Honeywell, USA). Set the mobile phase gradient as follows (A + B = 100%): 0–6 min, 0% B; 6–7.65 min, 0–1% B; 7.65-9.35 min, 1–6% B; 9.35–10 min, 6% B; 10–11 min, 6–10% B; 11–12 min, 10–20% B; 12–14 min, 20–50%; 14–17 min, 50–75% B; 17–17.5 min, 75–0% B; 17.5–30 min, 0% B. Step 45. Setup the Sciex QTRAP 6500+ mass spectrometer in a positive ion mode using multiple reaction monitoring scans. Set the parameters: Curtain Gas (CUR): 30.0; Collisionally Activated Dissociation Gas (CAD): 8.0; Ion Spray Voltage (IS): 5500.0; Temperature (TEM): 350.0; Gas 1 (Nebulizer Gas, GS1): 30.0; Gas 2 (Auxiliary Gas, GS2): 30.0. Step 46. Monitor the following mass transitions for nucleosides: m / z 244.1 to 112.1 for cytidine (C); m / z 268.1 to 136.1 for adenosine (A); m / z 284.1 to 152.1 for guanosine (G); m / z 282.1 to 150.1 for 1-methyladenosine (m1A); m / z 298.1 to 166.1 for 1-methylguanosine (m1G); m / z 258.1 to 126.1 for 3-methylcytidine (m3C); m / z 312.1 to 180.1 for N2,N2-dimethylguanosine (m2G). Optimize parameters of Declustering Potential (DP) and Collision Energy (CE) for each ion pair to enhance signal quality. Step 47. Transfer 40 µL of the mononucleoside sample from step 42 and a set of gradient- diluted nucleoside standard mixture into a 100 μL vial insert tube with 2 mL screw-top vial and carefully cap the vial. Step 48. Sequentially inject 10 µL of a set of gradient-diluted nucleoside standard mixture and AlkB-treated and untreated mononucleoside samples into the LC-MS / MS system to quantify the abundance of A, G, C, m1A, m1G, m2G and m3C. Determine the appropriate standard gradient range for each nucleoside to accurately cover the range of sample concentrations. Step 49. Calculate the molal concentration of each nucleoside according to the standard curve established for the same batch of samples. Ensure each nucleoside’s retention time is correctly identified, and the mass spectrometry peak is correctly calculated. Step 50. Normalize the molal concentration of m1A, m3C and m1G, m2G relative to A, C, and G respectively (FIG.10). Isolation of specified-size RNA from total RNAs (timing 2 days) was done using the following steps (steps 51–62) and steps 51-71 were performed under RNase-free conditions. The gloves and working area were cleaned with 75% ethanol and Nuclease Decontamination Solution. RNase-free water was used when indicated. Step 51. Purify total RNAs from tissue / cell samples by following steps 25–33. Step 52. Prepare the 15% Urea-PAGE gel by adding the following components in sequence (shown in Table 4): Urea, Acrylamide / Bis Solution, 10× TBE, and RNase-free water, without APS or TEMED. This can be done at room temperature. Agitate / shake occasionally until the urea completely dissolved. Assemble electrophoresis Chamber. Add APS and TEMED to the gel solution, mix thoroughly, and pour between the gel plates. Allow the gel to polymerize for at least 30 minutes. Add 1× TBE as running buffer. Pre-run the gel for at least 30 min at 200 V (constant voltage). Table 4. Reaction Components Component Volume / Mass 40% Acrylamide / Bis Solution, 19:1 3.75 mL Urea 4.2 g 10× TBE 1 mL TEMED 10 µL 10% APS 100 µL H2O Q.s. to10 mL in total Step 53. Add RNA sample (preferably no more than 2 µg in 10 µL RNase-free water) to an equal volume of RNA Loading Dye. Mix thoroughly. Heat at 75 °C for 5 min to denature the RNA and small RNA markers. Flush urea out of the wells with running buffer using a 1 mL tip, and then load RNA samples and small RNA markers into the wells. Run the gel for 40 min at 200 V (constant voltage) or until the blue dye reaches the bottom of the gel. Step 54. Dilute the SYBR™ Gold Nucleic Acid Gel Stain 10,000-fold with 1× TBE buffer to make a 1× staining solution. Remove the gel from the apparatus and incubate the gel in staining solution for 5 min. Step 55. Illuminate the stained gel on a UV transilluminator and excise the band (15– 50nt; FIG.11A–B). Place the gel slices into 1.5 mL microcentrifuge tube with 500 μL of gel elution buffer. Step 56. Freeze the tube containing the gel elution buffer and gel slices on dry ice or −80 °C for 15 min. Step 57. Incubate the tube overnight at room temperature with continuous rotation. Ensure the tube is properly sealed to prevent contamination and allow for thorough mixing of the gel components. Step 58. Freeze the tube containing the gel elution buffer and gel slices on dry ice or −80 °C for 15 min Centrifuge the tube at 13,000 × g for 10 min at room temperature. Draw off and save the supernatant in a clean microcentrifuge tube. Step 59. Freeze the tube containing the gel elution buffer and gel slices on dry ice or −80 °C for 15 min. Add 500 μL of phenol:chloroform:isoamyl alcohol to the supernatant and mix thoroughly. Centrifuge at 12,000 g for 10 min at 4°C. Draw off and save the supernatant in a clean microcentrifuge tube. Step 60. Add 500 μL of chloroform to the supernatant and mix thoroughly. Centrifuge at 13,000 g for 10 min at 4°C. Remove the supernatant. Step 61. Wash the pellet in 1 mL of 75% ethanol and centrifuge at 13,000 g for 20 min at 4°C. Decant the supernatant, removing as much as possible without disturbing the pellet. Dry the RNA pellet. Step 62. Resuspend the RNA pellet in 12 µL of RNase-free water by pipetting up and down several times until the RNA is completely dissolved. Quantify the concentration of small RNAs using either the Qubit microRNA assay kit or the Bioanalyzer 2100 with the small RNA kit, following the manufacturer’s instructions. The RNA should be used immediately or stored at −80 °C for future use. T4PNK enzyme treatment (timing about 4 hours) was done using the following steps (steps 63-64). Step 63. Prepare the T4PNK enzyme treatment by mixing the following components listed in Table 5. The listed volume is recommended for one reaction; prepare enough for all reactions plus 10% additional volume. Incubate at 37 °C for 20 min. Table 5. Reaction Components Component Volume Final concentration in 50 µL reaction Input RNA 10 µL variable T4 PNK Reaction Buffer (10×) 5 µL 1× ATP (10 mM) 5 µL 1 mM T4 PNK (10 units) 1 µL 0.2 units / µL H2O 29 µL Total 50 µL Step 64. Purify RNA by following steps 25–32. AlkB enzyme treatment (timing about 4 hours) was done using the following step (step 65). Step 65. Obtain the purified RNAs from step 64, proceed with the AlkB treatment in step 35. Subsequently, purify the RNA according to steps 25–32. During the AlkB treatment step, adjust the AlkB enzyme amount to match the input RNA amount at a ratio of 1:1, modifying the volume of water as necessary to maintain a total reaction volume of 50 µL. Optionally, the order of T4PNK enzyme treatment and AlkB enzyme treatment can be interchanged. Small RNA library construction and sequencing (timing about 7 days) was done using the following steps (steps 66-87). Step 66. Dilute the 3′-single-read (SR) adaptor (15 μM) to match the small RNA concentration proportionally. For example, if the average RNA molecule length is 35 nucleotides (nt) and the small RNA concentration is 5 ng / μL in a volume of 6 μL, the dilution rate of the adaptor can be calculated as follows:15 / (5 × 6 × 103 / (35 × 320.47 + 18.02)) ≈ 5.61. This calculation suggests diluting 1 μL of the 3′-SR adaptor with 4.61 μL of RNase-free water. Next, mix the following components shown in Table 6 and incubate at 70 °C for 2 minutes. Table 6. Reaction Components Component Volume Input small RNA 6 µL 3′-SR adaptor 1 µL Total 7 µL Step 67. Add and mix the following components shown in Table 7. Incubate at 16 °C for 18 h. Table 7. Reaction Components Component Volume 3′-Ligation reaction buffer 10 µL 3′-Ligation enzyme 3 µL Total 20 µL Step 68. Dilute the reverse transcription primer to the same rate calculated in previous step 66. Add and mix the following components shown in Table 8. Table 8. Reaction Components Component Volume RT primer 1 µL H2O 4.5 µL Total 25.5 µL Step 69. Place the mixture in a thermal cycler and run the following program shown in Table 9. Table 9. Reaction Temperatures Temperature Time 75 °C 5 min 37 °C 15 min 25 °C 15 min 4 °C ∞ Step 70. Incubate the 5′-SR adaptor at 70 °C for 2 minutes, then immediately place the tube on ice. Use the denatured adaptor within 30 min. Dilute the 5′-SR adaptor to the same rate calculated in previous step 66. Add the following components shown in Table 10 one by one without premixing. Incubate at 25 °C for 1 h. Table 10. Reaction Components Component Volume 5′-SR adaptor 1 µL 5′-ligation reaction buffer 1 µL 5′-ligation enzyme 2.5 µL Total 30 µL Step 71. Add and mix the following components shown in Table 11. Incubate at 50 °C for 1 h. Safe Stopping Point: If you do not plan to proceed immediately to PCR amplification, then heat inactivate the RT reaction at 70°C for 15 min. Samples can be safely stored at −15 °C to −25 °C for 24 h. Table 11. Reaction Components Component Volume First strand synthesis reaction buffer 8 µL RNase inhibitor 1 µL Reverse transcriptase 1 µL Total 40 µL Step 72. Add and mix the following components shown in Table 12. Table 12. Reaction Components Component Volume Taq DNA polymerase 50 µL SR primer 2.5 µL Index primer 2.5 µL H2O 5 µL Total 100 µL Step 73. Perform the PCR cycling step. Amplification conditions may vary depending on the RNA input amount, type of tissue, and species (Table 13). Typically, the number of PCR cycles can be estimated based on the small RNA input (x ng) using the following formula: PCR cycle = round(22 −log₂(x)). It is safe to store the library at −20 °C for 24 h after PCR. Avoid leaving the sample at 4°C overnight if possible. Table 13. Amplification Conditions Cycle Step Temp Time Cycles Initial denaturation 94 °C 30 sec 1 Denaturation 94 °C 15 sec Annealing 62 °C 30 sec variable Extension 70 °C 15 sec variable Final extension 70°C 5 min 1 End 4 °C ∞ Step 74. Purify the PCR amplified cDNA construct using a Monarch PCR & DNA Kit. Add 700 μL DNA cleanup binding buffer to the 100 μL sample. Mix well by pipetting up and down or by gently flicking the tube. Do not vortex. Insert the cleanup column into a 2 mL collection tube and load the mixture onto the column. Centrifuge at 16,000 × g for 1 min, then discard flow- through. Step 75. Re-insert the column into the same collection tube. Add 200 μL DNA wash buffer. Centrifuge at 16,000 × g for 1 min, then discarding flow-through. Repeat this step. Step 76. Transfer the column to a clean 1.5 mL microcentrifuge tube. Critical: Ensure that the column remains dry and does not come into contact with the flow-through. Step 77. Add 27.5 µl water to the center of the column matrix. Wait for 1 min, then centrifuge at 16,000 × g for 1 min. It is safe to store the library at −20 °C for 1 week. Step 78. Load 1 μL of the purified PCR products on the Bioanalyzer using the DNA 1000 kit according to the manufacturer’s instructions. Step 79. Prepare the 6% PAGE 10-well gel by adding the following components in sequence: Acrylamide / Bis Solution, 10× TBE and water, without APS or TEMED. This can be done at room temperature. Assemble electrophoresis Chamber. Add APS and TEMED to the gel solution, mix thoroughly, and pour between the gel plates. Allow the gel to polymerize for at least 30 min. Add 1× TBE as running buffer. The components are shown in Table 14. Table 14. Reaction Components Component Volume / Mass 30% Acrylamide / Bis Solution, 37.5:1 2 mL 10× TBE 1 mL 10% APS 100 μL TEMED 10 μL H2O 7 mL Step 80. Add 25 μL DNA sample with 5 μL gel loading dye (6×). Mix well. Load samples and DNA markers into the wells. Run the gel for 60 min at 120 V (constant voltage) or until the blue dye reaches the bottom of the gel. Step 81. Dilute the SYBR™ Gold Nucleic Acid Gel Stain 10,000-fold with 1× TBE buffer to make a 1× staining solution. Remove the gel from the apparatus and incubate the gel in staining solution for 5 min. Step 82. Illuminate the stained gel on a UV transilluminator and excise the band (~130– 170 bp; FIG.12A–B). Step 83. Place the gel slice from the same sample in 1.5 mL tube and crush the gel slices with the disposable pellet pestles and then soak in 250 μL DNA Gel Elution buffer (1×). Rotate end- to-end for at least 2 h at room temperature. Transfer the eluate and the gel debris to the top of a gel filtration column. Centrifuge the filter for 2 min at 17,000 × g. Step 84. Recover eluate and add 1 μL Linear Acrylamide, 25 μL 3 M sodium acetate, pH 5.5 and 750 μL of 100% ethanol. Vortex well. Precipitate in a dry ice / methanol bath or at −80 °C for at least 30 min. Spin in a microcentrifuge 14,000 × g for 30 min at 4 °C. Step 85. Remove the supernatant taking care not to disturb the pellet. Wash the pellet with 75% ethanol by vortexing vigorously. Spin in a microcentrifuge 14,000 × g for 30 min at 4 °C. Air dry pellet for up to 10 min at room temperature to remove residual ethanol. Resuspend pellet in 12 μL TE Buffer or EB buffer based on the sequencing center requirement. Step 86. Load 1 μL of the purified library on a 2100 Bioanalyzer using the DNA 1000 or high sensitivity DNA kit according to the manufacturer’s instructions (FIG. 12C–D). Check the size, purity, and concentration of the sample. The final concentration should yield at least 1 nM of purified cDNAs, or meet the specific requirements of sequencing centers. It is safe to store the library at −20 °C for 1 week. Step 87. Pool the cDNA samples in the desired ratio, and then sequence the libraries using the Illumina high-throughput sequencing platform. Typically, one sample requires 10 million sequencing reads. Small RNA-seq data analysis (timing about 1 day) was done using the following steps (steps 88–89). The parameters were replaced within the curly brackets in the remaining steps of the procedure with customized input files and computation resources. Step 88. Prepare the reference species annotation database. Software setup for small RNA-seq analysis was performed as follows. sncRNA reference database setup (standard sncRNA reference database): Most common sncRNA reference databases are precompiled and can be downloaded from the following: github.com / junchaoshi / sports1.1 / blob / master / precompiled_annotation_database.md. Customized sncRNA reference database: The sncRNA reference database may also be customized. Detailed setup instructions are available here: github.com / junchaoshi / sports1.1. Computational software installation: The small RNA annotation software SPORTS1.1 is designed for use in UNIX / Linux environments. The software can be obtained from GitHub via the following command: “$ git clone https: / / github.com / junchaoshi / sports1.1.git”. Alternatively, the package can be downloaded in a compressed file by the following command: “$ wget github.com / junchaoshi / SPORTS1.1 / archive / master.zip.” Additional Required Software: The following programs were installed according to the manufacturer’s instructions: Perl: perl.org; R: r-project.org; Bowtie: bowtie-bio.sourceforge.net / index.shtml Cutadapt: cutadapt.readthedocs.io / en / stable / index.html. The SPORTS1.1 software directory should be added to the “PATH” environment variable for ease of access, or the scripts can be executed directly from their full paths: “$ echo 'export PATH=$PATH:{your_path_to_SPORTS1.1- master} / source' >> ~ / .bashrc” “$ chmod 755 {your_path_to_SPORTS1.1- master} / source / sports.pl”. Step 89. Use the SPORTS1 tool (version 1.1.2) with customized settings to trim adaptor and low-quality bases from the raw sequencing data and annotate them to reference database. Below is an example for mouse samples: $ sports.pl -i {input_folder} -p {threads} -M 1 \ -a -x GTTCAGAGTTCTACAGTCCGACGATC -y TGGAATTCTCGGGTGCCAAGG \ -g {reference_database_folder} / Mus_musculus / genome / mm10 / genome \ - mmu \ rna \ -f {reference_database_folder} / Mus_musculus / Rfam / 12.3 / Rfam-12.3-mouse \ - 1-4 below. It is crucial to avoid RNA contamination and minimize RNA degradation in biological samples to achieve accurate small RNA profiles. The RNA Integrity Number (RIN) is commonly used to measure RNA integrity, but it may not be suitable for certain sample types, such as sperm, which typically lack the 18S and 28S rRNA peaks. For sperm samples, the following specialized procedure was used to extract RNAs from sperm and sperm heads. Step 1. Releasing mouse sperm from the cauda epididymis into 5 mL of PBS. Immediately incubate this suspension at 37 °C for 15 min. Filter 4 mL of the upper supernatant though a 40 µm cell strainer. This prepares the sample for subsequent steps, either to isolate sperm or sperm head. Step 2a. For sperm isolation, transfer the filtered flow-through into 8 mL of sperm isolation buffer and incubate it on ice for 40 min. Centrifuge the mixture at 600 × g for 5 minutes at 4 °C and then discard the supernatant. Step 2b. For sperm head isolation, centrifuge the flow-through at 3,000 × g for 5 min at 25 °C and discard the supernatant. Add 2 mL of sperm head isolation buffer and incubate at room temperature for 15 min. Follow this with another centrifugation at 3,000 × g for 5 minutes at 25 °C, and then discard the final supernatant. Step 3. For both sperm and sperm head pellets, wash the pellet by resuspending in 5 mL of PBS, then centrifuge at 600 × g for 5 min at 4 °C. Carefully discard the supernatant. Add at least 1 mL of TRIzol Reagent for every 5 × 106cells to ensure adequate coverage. Achieve complete lysis by repeatedly passing the suspension through a syringe fitted with a 27G needle until no visible precipitate remains. Step 4. Purify RNAs from the mixture following steps 25–32. The PANDORA-seq experimental procedure could be monitored at several steps. At Step 33, the concentration and length of total RNAs must be measured using an Agilent 2100 Bioanalyzer with an RNA Nano 6000 kit to assess RNA integrity. Typically, an RNA Integrity Number (RIN) of 8 or higher is indicative of high-quality RNA samples. Typically, an RNA Integrity Number (RIN) of 8 or higher is indicative of high-quality RNA samples. However, in contrast to somatic tissues, high-quality mouse and human sperm and sperm head samples lack the 18S and 28S rRNA peaks, resulting in an unavailable or very low RIN value. Following sncRNA isolation procedure at Step 62, the quantity of small RNAs should also be measured, either using Qubit microRNA assay kits (range from 0.5−150 ng / µL) or the Agilent 2100 Bioanalyzer Small RNA kit (range from 0.05–2 ng / µL). The small RNA libraries should achieve a concentration of at least 1 nM or meet the specific requirements of the sequencing centers by Step 86. These libraries typically exhibit a peak within the range of about 130-170 bp. We provided three biological replicates of mouse sperm and liver PANDORA-seq libraries, obtaining approximately 20 million reads on average. Following the SPORTS1.1 annotation process, 93.9% and 96.5% of the reads from sperm and liver, respectively, were successfully annotated. For mouse sperm, 82.1% of reads mapped directly to the rRNA sequence, whereas 2.6% of reads were mapped to tRNAs. In contrast, 48.6% of reads from mouse liver mapped to the rRNA sequence, with 4.0% mapping to tRNAs. Given the wide variation in RNA species across different tissues and cell types, a high sequencing depth is recommended to ensure adequate coverage for low-expressed sncRNAs. We identified 29,675 types of annotated sncRNAs in mouse sperm and 22,422 types in mouse liver, with an expression level of RPM ≥1. Additionally, a set of three biological replicates of traditional sncRNA libraries were provided for mouse sperm and liver. From this set, approximately 17 million reads were obtained. Following a data processing approach similar to that described above, 98.4% and 98.7% of reads from sperm and liver, respectively, could be annotated. 18,883 types of annotated sncRNAs were identified in mouse sperm and 10,187 in mouse liver, with an expression level of RPM ≥1. Example 2 Materials and Methods Animals. Animal experiments were conducted under the protocol and approval of the institutional animal care and use committees of the University of California, Riverside. Mice were given access to food and water ad libitum and were maintained on a 12 h light / 12 h dark artificial lighting cycle. Mice were housed in cages at a temperature of 22–25 °C, with 40–60% humidity. Mouse sperm samples during aging. Mature sperm were collected from the cauda epididymis of male C57BL / 6J mice, aged at 10-, 30-, 50-, 70- and 90-weeks, and were released into 5 mL of phosphate-buffered saline (PBS). This mixture was then incubated at 37 °C for 15 minutes, followed by filtration through a 40-µm cell strainer to remove tissue debris. To eliminate somatic cell contamination, the filtered sperm were incubated with a somatic cell lysis buffer (comprising 0.1% sodium dodecyl sulfate (SDS) and 0.5% Triton X-100 in nuclease-free water) for 40 minutes on ice. Subsequently, the sperm were pelleted by centrifugation at 600 × g for 5 minutes. The sperm pellet was then resuspended in 10 mL PBS, washed, and centrifuged twice at 600 g for 5 minutes each time. Finally, RNA isolation was performed on the precipitated sperm. Longitudinal human sperm samples. Study participants of known fertility for the longitudinal cohort gave informed consent for semen samples to be used for research under University of Utah IRB # 0012049. From this cohort, 8 donors each provided two semen samples over a span of 10–19 years, with their ages at the time of each collection detailed in FIG. 3A. Another two donors, each provided two semen samples with short-term intervals within one month. All semen samples were collected by masturbation following 2–5 days of sexual abstinence and were subsequently cryopreserved in commercially available sperm cryopreservation media (TYB media; Irvine Scientific) and stored in liquid nitrogen until use in the study. Total RNA isolation. TRIzol® reagent (1 mL; Invitrogen) was added to microtubes containing sperm and sperm head samples, followed by uniform vortexing. The samples were then incubated at room temperature for 5 minutes. To each milliliter of the sample, 200 µL of chloroform (Alfa Aesar) was added, vortexed for 15 seconds, incubated at room temperature for 2 minutes, and then centrifuged at 12,000 × g for 15 minutes at 4 °C. The aqueous phase was transferred to a new microtube and mixed with an equal volume of isopropanol (Fisher Scientific). After mixing, the samples were incubated at room temperature for 10 minutes, followed by centrifugation at 12,000 × g for 10 minutes at 4°C. The supernatant was discarded, and the pellet was washed with 1 mL of 75% ethanol (Koptec), then centrifuged at 7,500 × g for 5 minutes at 4 °C. The supernatant was removed, and the pellet was air-dried for 5 minutes. Finally, the pellet was resuspended in nuclease-free water, quantified, and either stored at −80 °C or used for further analyses. Isolation of 15-50 nt RNA fraction from total RNAs. The RNA sample, mixed with an equal volume of 2× RNA loading dye (New England Biolabs), was incubated at 75 °C for 5 min. The mixture was loaded into 15% (wt / vol) urea polyacrylamide gel (10 mL mixture containing 7 M urea (Invitrogen), 3.75 mL Acrylamide / Bis 19:1, 40% (Ambion), 1 mL 10× TBE (Invitrogen), 1 g /  L ammonium persulfate (Sigma-Aldrich) and 1 mL TEMED (Thermo Fisher Scientific). The gel was run in a 1× TBE running buffer at 200 V until the bromophenol blue reached the bottom of the gel. After staining with SYBR Gold solution (Invitrogen), gel that contained small RNAs of 15– 50 nucleotides was excised based on small RNA ladders (New England Biolabs and Takara) and eluted in 0.3 M sodium acetate (Invitrogen) and 100 U / mL RNase inhibitor (New England Biolabs) overnight at 4 °C. The sample was then centrifuged for 10 min at 12,000 × g (4 °C). The aqueous phase was mixed with pure ethanol, 3 M sodium acetate and linear acrylamide (Invitrogen) at a ratio of 3:9:0.3:0.01. Then, the sample was incubated at −20°C for 2 h and centrifuged for 25 min at 12,000 × g (4 °C). After removing the supernatant, the precipitation was resuspended in nuclease-free water, quantified and stored at −80 °C or used for further processing. The RNAs were then separated into two halves, one for PANDORA-seq, the other for traditional sncRNA- seq. PANDORA-seq. RNA fragments ranging from 15-50 nucleotides (nt) were incubated in a 50 μL reaction mixture containing 5 μL 10× PNK buffer (New England Biolabs), 1 mM ATP (New England Biolabs), 10 U T4PNK (New England Biolabs) at 37 °C for 20 min. Following this incubation, the reaction mixture was added to 500 µL of TRIzol® reagent (Invitrogen) to perform the RNA isolation procedure. Then the purified RNA was incubated in 50 μL reaction mixture containing 50 mM HEPES (pH 8.0) (Gibco and Alfa Aesar), 75 μM ferrous ammonium sulfate (pH 5.0), 1 mM α-ketoglutaric acid (Sigma–Aldrich), 2 mM sodium ascorbate, 50 mg / L bovine serum albumin (Sigma-Aldrich), 4 μg / mL AlkB, 2,000 U / mL RNase inhibitor at 37°C for 30 min. Subsequently, this reaction mixture was also added to 500 µL of TRIzol® reagent (Invitrogen) to perform the RNA isolation procedure, followed by small RNA library construction and deep sequencing. Traditional sncRNA-seq. 15-50 nt RNA fraction were directly processed for small RNA library construction and deep sequencing. Small RNA library construction and deep sequencing. The adapters were sourced from the NEBNext Small RNA Library Prep Set for Illumina (New England Biolabs) and were ligated in sequence. First, a 3′ adapter was added under the following reaction conditions: incubation at 70 °C for 2 minutes, followed by 16 °C for 18 hours. Subsequently, a reverse transcription primer was introduced under these conditions: 75 °C for 5 minutes, 37 °C for 15 minutes, and then 15 °C for 15 minutes. Next, a 5′ adapter mix was added, with the reaction conditions set at 70 °C for 2 minutes and then 25 °C for 1 hour. First-strand cDNA synthesis proceeded at 70 °C for 2 minutes and then 50 °C for 1 hour. PCR amplification was carried out to enrich the cDNA fragments, utilizing the PCR Primer Cocktail and PCR Master Mix under the following conditions: an initial denaturation at 94 °C for 30 seconds; followed by 14–23 cycles of denaturation at 94 °C for 15 seconds, annealing at 62 °C for 30 seconds, and extension at 70 °C for 15 seconds; with a final extension at 70 °C for 5 minutes and then a hold at 4 °C. The libraries were then amplified and sequenced using the SE100 strategy on an Illumina system by the University of California, San Diego IGM Genomics Center. Processing the PANDORA-seq and traditional sncRNA-seq data. The SPORTS tool (v1.1.1) (Shi et al., Genomics Proteomics Bioinformatics 16: 144-151(2018)) was used to annotate and summarize the PANDORA-seq data and traditional sncRNA-seq with one mismatch tolerance. Reads per million (RPM) was used to measure the expression of the individual sncRNAs categories, including tsRNAs, rsRNAs, and miRNAs. For tsRNAs, only the RNA species derived from mature tRNAs were included for further analysis. miRNAs, tsRNAs, and rsRNAs associated with sperm aging in mouse. To prioritize the sperm sncRNAs that are associated with aging, the expression of the individual sncRNA categories from mouse sperm / sperm heads across five developmental stages (10-wk, 30-wk, 50- wk, 70-wk, and 90-wk) were analyzed. Spearman’s rank correlation test was used to identify the sncRNAs that are associated with aging. The computed P-values were adjusted using the Benjamini-Hochberg procedure. The mouse aging-associated sncRNAs that can be mapped to human genome were prioritized for sperm and sperm heads separately. The development of STAR aging signature. The STAR signature is composed of 40 tsRNA / rsRNA categories. A weight was assigned to each sncRNA family within the signature as below: ^^ െ1, ^^^ ^ 0^ ^ ^ 0 where wi is the weight of sncRNA i and ρi is the correlation coefficient between the expression of sncRNA i and developmental stage in mouse. An aging score can be further computed for each sample as below: ^ ^^ ൌ ^ ^^^ ∙ ln ^^^^ ^ 1^where s is the aging score; n is the in the signature (here n = 40); wi is the weight of sncRNA i; ei is the i; “ln” stands for natural logarithm transformation. Validating the STAR signature in human. The aging score was further computed for the human samples based on the STAR signature. Here, donors with only two time points were focused on: the earlier time point (T1) and the later time point (T2). Spearman’s rank correlation test was applied to investigate the relationship between real age and predicted aging score (based on the STAR signature). Paired comparison between the T1 and T2 samples was performed using paired t-test. Statistical analyses. All the statistical analyses were conducted using the R programming platform. The Spearman’s correlation test and t-test were performed using the “cor.test” and “t- test” functions, respectively. The principal coordinate analysis (PCoA) was performed using the “pcoa” function within the “ape” package. The dissimilarity indices were computed using the “vegdist” function within the “vegan” package based on the “clark” method. Example 3 The Aging Cliff in Samples from Mice To analyze the sperm aging process in mice, the inbred strain C57BL / 6J from Jackson Laboratory was used, the mice were organized into five age groups at 20-week intervals (10-, 30-, 50-, 70-, and 90-week-old; FIG.1A). Mature sperm from the cauda epididymis (four mice per age group) were isolated for RNA extraction as previously described. See Peng et al., Cell Res. 22: 1609-1612 (2012). The extracted sperm RNA from each individual was split into two portions: one for traditional sncRNA-seq and the other for PANDORA-seq. Sequencing data were annotated for different sncRNA types (e.g., miRNAs, tsRNAs, rsRNAs) using the SPORTS tool (see Shi et al., Genomics Proteomics Bioinformatics 16: 144-151(2018)) and further analyzed for age-related changes in sncRNA signatures. Through principal coordinate analysis (PCoA) of both PANDORA-seq (FIG.1B) and traditional sncRNA-seq data (FIG.1C), a distinct “aging cliff” in the PANDORA-seq data was observed, occurring between the 50-week and 70-week intervals, which marked a dramatic shift in tsRNA / rsRNA composition by computing every category of tsRNA and rsRNA expression. This stark change delineated the early (10–50 weeks) from the later stages (70–90 weeks). However, such a clear demarcation was not discernible in the traditional sncRNA- seq data in regards of tsRNA and rsRNA expression. As an illustration of the expression pattern of individual tsRNAs / rsRNAs (FIG.1D), genomic tsRNA-Arg-CCT, mitochondrial tsRNA-His-GTG, and rsRNA-18s at each age stage demonstrated substantial alterations between the 50- and 70- week samples in the PANDORA-seq analysis, recapitulating the “aging cliff” represented by all overall tsRNA / rsRNA profile (FIG.1B). In addition, a similar PCoA analysis was conducted based on sperm miRNA composition by computing every miRNA expression. Intriguingly, even though miRNA reads constitute <0.5% of total reads in PANDORA-seq compared to >5% in traditional sncRNA-seq (Table 15), miRNA signature from PANDORA-seq still delineated an aging cliff between the 50-week and 70-week intervals, which was not observed in traditional sncRNA-seq (FIG. 2A-C). One possible explanation for this observation is that a subset of the miRNAs detected by PANDORA-seq are actually derived from tsRNAs and rsRNAs, a phenomenon the inventors have previously reported. See Shi et al., Nature Cell Biol.23: 424-436 (2021). Even so, this result is somewhat unexpected given the low proportion of miRNA reads in the PANDORA-seq dataset, and it highlights the superior sensitivity of PANDORA-seq in detecting age-related changes within sncRNA populations, even when these changes are represented by a relatively minor fraction of the total reads. However, it should be noted that the “aging cliff” identified by miRNAs between the 50- week and 70-week intervals is less pronounced than that revealed by tsRNAs / rsRNAs. This difference is supported by the ratio of between-group variance to within-group variance (F- statistic), which is significantly higher for tsRNAs / rsRNAs compared with miRNAs (FIG. 2D), supporting a better separation between the demarcated stages (early 10- / 30- / 50-week vs. later 70- / 90-week) using tsRNA / rsRNA profile. Table 15. miRNA / tsRNA / rRNA Proportion in Mouse Sperm and Sperm Heads Traditional sncRNA-seq PANDORA-seq Sperm Time miRNA (%) tsRNA (%) rsRNA (%) miRNA (%) tsRNA (%) rsRNA (%) 10-wk 6.3 ± 1.3 28.1 ± 1.5 30.5 ± 1.4 0.1 ± 0.0 6.9 ± 0.7 74.0 ± 0.8 30-wk 6.4 ± 1.1 31.6 ± 1.8 28.2 ± 1.7 0.1 ± 0.0 9.0 ± 0.8 70.5 ± 1.0 50-wk 7.3 ± 1.7 24.7 ± 0.5 31.9 ± 1.6 0.1 ± 0.0 7.0 ± 0.7 70.3 ± 1.2 70-wk 7.2 ± 1.5 15.5 ± 1.6 37.6 ± 8.3 0.2 ± 0.0 4.8 ± 0.3 67.6 ± 2.1 90-wk 9.6 ± 2.1 18.9 ± 2.5 33.3 ± 6.9 0.1 ± 0.0 6.1 ± 0.7 65.8 ± 0.7 Sperm Head 10-wk 1.1 ± 0.1 44.2 ± 4.4 18.2 ± 1.5 0.2 ± 0.0 18.5 ± 1.5 50.7 ± 1.5 30-wk 1.5 ± 0.2 37.3 ± 4.5 19.1 ± 1.7 0.3 ± 0.0 19.0 ± 2.2 48.1 ± 1.6 50-wk 1.5 ± 0.2 36.0 ± 3.6 20.9 ± 0.9 0.2 ± 0.1 17.8 ± 1.8 48.9 ± 1.2 70-wk 2.4 ± 0.5 21.9 ± 5.7 40.8 ± 6.5 0.2 ± 0.1 16.0 ± 2.4 48.3 ± 1.0 90-wk 1.1 ± 0.2 23.9 ± 6.0 43.9 ± 6.4 0.1 ± 0.0 16.1 ± 1.0 53.2 ± 1.8 Example 4 Analysis of De-Membranated Sperm Building on PANDORA-seq’s superior capacity to discern aging signatures in mice, its application was extended to human sperm samples from a unique longitudinal cohort of 8 donors – with each donor provided two semen samples over a 10–19-year span, yielding 8 pairs (16 samples) with ages ranging from 34 to 70 years (FIG.3A). Consistent with the variability observed in human semen samples, during preparation for RNA extractions, sample-to-sample variability was encountered—some sperm samples exhibited increased viscosity, or cell surface contamination with debris that was difficult to remove despite extensive washing, and other sperm contained cytoplasmic droplets that encapsulate various sncRNAs, potentially confounding the purity of the sperm sncRNA signature. To address these issues and ensure the integrity / consistence of the RNA samples, a refined protocol that de-membranates sperm was employed by removing the tail, providing an uncontaminated source of RNA from the sperm heads alone. This distinction could be physiologically significant, because at fertilization, while RNAs from the sperm’s cytoplasm are quickly diluted in the oocyte’s cytoplasm, RNAs embedded in the sperm head are deeply integrated within the nucleus and could play a direct role in the epigenetic reprogramming of the male pronucleus, which is important in regulating embryo development. Based on the above rationale, PANDORA-seq and traditional sncRNA-seq was performed on de-membranated mouse sperm heads across the same age groups as studied with whole sperm (10-, 30-, 50-, 70-, and 90-week), and on de-membranated human sperm from our longitudinal cohort of 8 donor pairs (FIG.3A). PANDORA-seq again identified a clear “aging cliff” at the 50–70 week transition in mouse sperm heads, a pattern not observed with traditional sncRNA-seq (FIG.4). Interestingly, while there are differences in the tsRNA / rsRNA composition between sperm heads and whole sperm, both identified aging signatures indicative of this “aging cliff”. Quantitative analyses revealed a partial overlap in aging-associated tsRNA / rsRNA signature: sperm heads displayed a signature comprising 40 tsRNAs / rsRNAs, the whole sperm showed a composition of 24 tsRNAs / rsRNAs, among which 15 were common to both. To align with the human sperm heads samples, the 40-RNA signature of tsRNAs / rsRNAs associated with aging in mouse sperm heads was used (Table 16), which was termed the sperm tsRNA and rsRNA (STAR) aging signature (FIG.3B). Table 16. STAR-signature: tsRNA / rsRNA Aging Signature in Mouse Sperm Head by PANDORA-seq tsRNA / rsRNA family Correlation Raw P-value Adjusted P- Weight coefficient (ρ) value 5S-rRNA −0.763 1.4 × 10−4 1.1 × 10−3−1 28S-rRNA 0.639 3.2 × 10−3 1.0 × 10−21 mature-tRNA-Arg-ACG −0.667 1.8 × 10−3mature-tRNA-Arg-CCG −0.569 1.1 × 10−2mature-tRNA-Arg-CCT −0.732 3.7 × 10−4mature-tRNA-Arg-TCG −0.586 8.4 × 10−3mature-tRNA-Arg-TCT −0.835 8.8 × 10−6mature-tRNA-Gln-CTG −0.617 4.9 × 10−3mature-tRNA-Gln-TTG −0.789 5.9 × 10−5mature-tRNA-Gly-TCC −0.658 2.2 × 10−3mature-tRNA-Ile-TAT −0.691 1.1 × 10−3 mature-tRNA-Leu-CAA −0.799 4.1 × 10−5mature-tRNA-Leu-CAG −0.762 1.5 × 10−4mature-tRNA-Leu-TAA −0.695 9.5 × 10−4mature-tRNA-Leu-TAG −0.584 8.6 × 10−3mature-tRNA-Lys-TTT −0.512 2.5 × 10−2mature-tRNA-Met-CAT −0.702 8.0 × 10−4mature-tRNA-Phe-GAA −0.784 7.2 × 10−5mature-tRNA-Pro-AGG −0.584 8.6 × 10−3mature-tRNA-Pro-CGG −0.584 8.6 × 10−3mature-tRNA-Pro-TGG −0.584 8.6 × 10−3mature-tRNA-Ser-CGA −0.793 5.2 × 10−5mature-tRNA-Ser-GCT −0.862 2.0 × 10−6mature-tRNA-Thr-AGT −0.590 7.9 × 10−3mature-tRNA-Thr-CGT −0.682 1.3 × 10−3mature-tRNA-Thr-TGT −0.721 4.9 × 10−4mature-tRNA-Trp-CCA −0.750 2.2 × 10−4mature-tRNA-Tyr-GTA −0.885 4.9 × 10−7mature-mt_tRNA-Arg-TCG −0.548 1.5 × 10−2mature-mt_tRNA-Gln-TTG −0.534 1.8 × 10−2mature-mt_tRNA-Glu-TTC −0.679 1.4 × 10−3mature-mt_tRNA-Ile-GAT −0.672 1.6 × 10−3mature-mt_tRNA-Leu-TAA −0.548 1.5 × 10−2mature-mt_tRNA-Met-CAT −0.738 3.1 × 10−4mature-mt_tRNA-Phe-GAA −0.717 5.5 × 10−4mature-mt_tRNA-Pro-TGG −0.563 1.2 × 10−2mature-mt_tRNA-Ser-GCT 0.554 1.4 × 10−2mature-mt_tRNA-Ser-TGA −0.584 8.6 × 10−3mature-mt_tRNA-Thr-TGT −0.593 7.4 × 10−3mature-mt_tRNA-Val-TAC −0.750 2.2 × 10−4 Based on this STAR signature, a scoring algorithm was developed to compute an aging score for each sample. The STAR aging signature was applied to score the human sperm heads samples, a significant positive correlation was discovered between actual donor age and the predicted aging score (Spearman’s rank correlation test: ρ = 0.596 and P = 0.015; FIG. 3B). Paired comparisons from each donor revealed an increased aging score in the later collection (T2) compared to the earlier one (T1) (paired t-test: P = 0.011; FIG.3D). Notably, the mouse 50- 70-week period coincides with the human age range of 40-50 years according to the estimated age-matching data from The Jackson Laboratory (FIG.3A). In addition to the donor samples with long-term intervals, two additional donors were further analyzed with semen collections with short- term intervals (with two months apart) and found the aging scores between the two collections to be very similar (FIG.5A and FIG.5B), further supporting the reliability and robustness of the STAR aging signature. Example 5 Comparison of Aging Signatures Between Mice and Humans The observed cross-species correlation in tsRNA / rsRNA-based aging signatures, although unexpected, suggests an evolutionarily conserved aging mechanism. This could be attributed to the highly conserved sequences of tRNA / rRNA and common factors in tsRNA / rsRNA biogenesis and regulation shared between mice and humans. In contrast, miRNA signatures from PANDORA-seq, while indicative of an aging cliff in mouse sperm heads, did not correlate with human sperm aging (FIG. 6). This lack of correlation might be due to the lower conservation levels of miRNAs between mice and humans. Additionally, tsRNA / rsRNA signatures selected from traditional sncRNA-seq that can show aging correlation in mice failed to predict human aging (FIG.7). This is most likely because traditional sequencing methods capture only a limited subset of tsRNAs and rsRNAs, predominantly those with fewer modifications. These findings highlight the unique cross-species sensitivity and specificity of the STAR aging signature as unveiled exclusively by PANDORA-seq. Finally, the changes in sncRNA signatures observed with aging may result from either altered biogenesis and regulation within the male germ cells, or from sncRNAs delivered by somatic cells that reflect systemic aging, or a combination of both. Although the precise mechanisms await further investigation, the predictability of the mouse-derived STAR aging signature in human sperm aging is promising. Extending its validation to a wider array of human samples could pave the way establishing a clinically relevant sncRNA-based biomarker. Such a biomarker, serving as a sperm epigenetic aging clock, has the potential to guide informed reproductive decisions and reduce the transmission of age-related health issues to offspring. Example 6 Isolation and De-Membranation of Human Sperm Heads Since human semen conditions from different individuals vary significantly, analyzing de- membraned sperm heads, instead of whole sperm, will efficiently eliminate potential contaminations due to variable semen conditions, and is essential to ensure the accuracy for examining sperm RNA code. The following protocol was used to isolate human sperm heads and de-membrane the sperm heads. A 1 mL sample of human sperm was centrifuged in a 15 mL tube at 600 × g for 5 minutes at 4 °C. Following centrifugation, the supernatant was carefully removed and discarded, leaving a pellet in the tube. 5 mL of PBS was added to the tube, the sperm was gently resuspended and was incubated in the PBS at 37 °C for 10 minutes. The mixture was transferred to a new 15 mL tube by filtering it through a 40 μm cell strainer. The mixture was centrifuged at 3,000 × g for 5 minutes at room temperature (i.e., from about 20 °C to about 22 °C). The supernatant was removed and discarded, leaving a pellet in the tube. 2 mL of sperm head lysis buffer was added to the tube and incubated at room temperature for 15 minutes. This step facilitates the separation of sperm heads from the bodies and induces lysis of the sperm head membranes. The sperm head is removed from the body (the tail has been removed / disrupted / dissolved), and also at this point the membrane has been removed from the sperm head. The remaining steps isolate and / or purify the isolated and de-membranated sperm heads. The tube was centrifuged again at 3,000 × g for 5 minutes at room temperature. The supernatant was removed and discarded, leaving a pellet in the tube. 5 mL of PBS was added to the tube, and it was centrifuged at 600 × g for 5 minutes at 4 °C. The supernatant was removed and discarded, leaving a pellet in the tube. The foregoing step was performed twice to ensure thorough washing. 1 mL of PBS was added to the pellet and the mixture was transferred to a 1.5 mL tube. The tube was centrifuged at 10,000 × g for 5 minutes at 4 °C. After centrifugation, the supernatant was removed and discarded, leaving a pellet in the tube. 1 mL of TRIzol® reagent (Invitrogen) was added to the pellet. A 27G syringe was used to lyse any remaining cellular debris.

Claims

CLAIMS What is claimed is:

1. A method for determining the quality of sperm in a sperm sample comprising: (i) extracting RNA from the sperm; (ii) obtaining an RNA expression profile of the sperm using PANoramic RNA Display by Overcoming RNA modification Aborted sequencing (PANDORA-seq); (iii) predicting the quality of the sperm using a statistical prediction algorithm generated from a set of RNA expression profiles obtained from sperm in a plurality of sperm samples from a plurality of subjects of known and varying ages, wherein the set of RNA expression profiles were generated using PANDORA-seq.

2. The method of claim 1, further comprising incubating the sperm in a sperm head lysis buffer to remove the tail and the membrane from the heads of the sperm prior to extracting the RNA from the sperm.

3. The method of claim 2, further comprising filtering the sperm through a 40 µm cell strainer prior to incubating the sperm in the sperm head lysis buffer.

4. The method of claim 1, further comprising treating the extracted RNA with ALKB and T4 PNK enzymes prior to generating the RNA expression profiles.

5. The method of claim 1, wherein the extracted RNA includes tsRNAs, rsRNAs, ysRNAs, snsRNAs, snosRNAs, or combinations thereof.

6. The method of claim 1, wherein the statistical prediction algorithm comprises a boxplot that plots the biological age of the plurality of subjects as a function of a variable aging score that statistically correlates the expression levels of tsRNAs and rsRNAs as a function of the ages of the plurality of subjects.

7. The method of claim 6, wherein predicting the quality of the sperm comprises calculating an aging score for the sperm based on the RNA expression profile of the sperm.

8. The method of claim 1, wherein the method further comprises generating a transformed statistical prediction algorithm from a modified set of RNA expression profiles.

9. The method of claim 8, wherein the statistical prediction algorithm continuously transforms as the set of RNA expression profiles continuously transforms as input data is received by one or more statistical or machine learning models.

10. The method of claim 1, further comprising biochemically treating the sperm sample in a manner that manipulates RNA expression of the sperm in the sperm sample.

11. A method for obtaining an RNA expression profile of sperm in a sperm sample comprising: (i) incubating the sperm in a sperm head lysis buffer to remove the membrane from the heads of the sperm; (ii) extracting RNA from the de-membraned heads of the sperm; and (iii) obtaining an RNA expression profile of the sperm.

12. The method of claim 11, further comprising filtering the sperm through a 40 µm cell strainer prior to incubating the sperm in the sperm head lysis buffer.

13. The method of claim 11, further comprising treating the extracted RNA with ALKB and T4 PNK enzymes prior to obtaining the RNA expression profile.

14. The method of claim 11, wherein obtaining the RNA expression profile comprises using PANoramic RNA Display by Overcoming RNA modification Aborted sequencing (PANDORA-seq).

15. A method for performing an in vitro fertilization or artificial insemination procedure comprising: obtaining a plurality of sperm samples from a plurality of subjects; assessing the quality of sperm in each sperm sample according to the method of any of claims 1-9; performing the in vitro fertilization or artificial insemination procedure with a sperm sample that has been determined to have high quality or that has been biochemicallytreated in a manner that manipulates the RNA expression of the sperm in the sperm sample.

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