High-throughput automated plasma small RNA library construction method and application thereof

Abstract

The application provides a high-throughput automatic plasma small RNA library construction method and application thereof, and particularly relates to construction of a high-throughput automatic plasma small RNA library, and the high-throughput automatic plasma small RNA library construction method can be used for quantitative research on plasma small RNA expression level and mining of potential biomarkers related to diseases. By using the optimized method of the application, library construction can be completed on 96 trace small RNA samples with a starting amount of less than 5 ng in one day, and high-quality data can be obtained after high-throughput sequencing. Through performance evaluation analysis, the technology of the application has good batch-to-batch stability, good distinguishing ability for the inherent biological differences of small RNA expression levels of different biological samples, and can be used for small RNA library construction experiments of super large cohorts.

Description

Technical Field

[0001] This invention belongs to the field of biotechnology. Specifically, this invention relates to a high-throughput automated method for constructing plasma small RNA libraries and its applications. Background Technology

[0002] Extracellular small RNAs refer to RNA molecules with a length of less than 200 nucleotides outside the cell, including miRNA, γ-RNA, circRNA, and lncRNA. The expression level of plasma-derived small RNAs is closely related to the physiological state of disease, and due to their advantages such as easy sampling, high stability, and rapid quantification, they have become an important candidate source of clinical biomarkers.

[0003] Small RNA sequencing (small RNA-seq) is a sequence analysis method based on high-throughput sequencing technology. Due to its high accuracy and wide detection range, it has become an important tool for discovering small RNA biomarkers. Using plasma samples from large-scale clinical cohorts, researchers can use small RNA-seq to comprehensively quantify small RNAs in plasma samples from patient and control groups, identifying differentially expressed small RNAs as potential biomarker candidates. Therefore, high-quality small RNA-seq data is a necessary prerequisite for biomarker discovery.

[0004] However, batch-to-batch stability has become a bottleneck issue for generating large-scale cohort small RNA data. Similar to RNA-seq, small RNA-seq data also faces the problem of systematic differences between batches, i.e., batch effects. Severe batch effects can lead to the overriding of biological signals; therefore, batch-to-batch quantitative stability is essential for small RNA-seq quantitative methods. However, the experimental procedures for small RNA-seq library construction are complex, and library quality is highly susceptible to operational details. Traditional manual operations, due to limitations such as low throughput and susceptibility to operational errors, are difficult to meet the needs of large-scale cohort library construction.

[0005] Therefore, there is an urgent need in this field to develop a high-throughput, automated method for constructing plasma small RNA libraries to improve experimental efficiency, batch-to-batch reproducibility, and reduce the possibility of operational errors during experiments. Summary of the Invention

[0006] The purpose of this invention is to provide a high-throughput, automated method for constructing plasma small RNA libraries.

[0007] The first aspect of this invention provides a high-throughput automated method for constructing small RNA libraries, using... Library construction using a small RNA-seq kit includes the following steps:

[0008] (1) Provide isolated small RNA samples;

[0009] (2) 3' adapters are ligated into the small RNA samples to obtain 3' adapter-containing samples.

[0010] (3) Remove excess 3' adapters from samples with 3' adapters, and add adapter depletion solution, magnetic beads, and isopropanol. The volume ratio of the reagents to the sample is 1.05-2:1-3:2.5-4:1.

[0011] (4) Deactivate the excess 3' connector;

[0012] (5) Connect the sample obtained in step (4) with a 5' connector to obtain a sample with a 5' connector;

[0013] (6) Reverse transcribe the sample obtained in step (5) to obtain a cDNA sample with adapters at both ends.

[0014] (7) Purify the sample obtained in step (6);

[0015] (8) Perform library amplification on the purified sample obtained in step (7) to obtain amplification products;

[0016] (9) The amplification product is purified and a library is constructed to obtain a small RNA library.

[0017] A second aspect of this invention provides an automated method for small RNA library construction, using... Library construction using a smallRNA-seq kit includes the following steps:

[0018] (1) Provide isolated small RNA samples;

[0019] (2) The small RNA in the small RNA sample is ligated with a 3' adapter to obtain a sample with a 3' adapter;

[0020] (3) Remove the unconnected redundant 3' connectors from the sample obtained in step (2);

[0021] (4) Deactivate the unconnected redundant 3' connectors in the sample obtained in step (3);

[0022] (5) Connect the sample obtained in step (4) with a 5' connector to obtain a sample with a 5' connector;

[0023] (6) Reverse transcribe the sample obtained in step (5) to obtain a cDNA sample with adapters at both ends.

[0024] (7) Purify the sample obtained in step (6);

[0025] (8) Perform library amplification on the purified sample obtained in step (7) to obtain amplification products;

[0026] (9) The amplification product is purified and a library is constructed to obtain a small RNA library;

[0027] The feature is that the purification steps (7) and / or (8) do not involve the steps of enriching short fragments and removing long fragments, but instead directly perform magnetic bead purification.

[0028] In another preferred embodiment, step (7) purification comprises the following steps:

[0029] (7-1) In step (6), the adapter depletion solution, magnetic beads and isopropanol are added sequentially to the reverse transcription product and incubated. The volume ratio of the reverse transcription product to the adapter depletion solution, magnetic beads and isopropanol is 1:0.2-0.4:0.8-1.2:2-3.

[0030] (7-2) Add 6-9 times the volume of the product from step (7-1) to wash;

[0031] (7-3) Add 1-1.2 times the volume of the product from step (7-1) to the product from step (7-2), mix well to resuspend the magnetic beads, let stand, and collect the supernatant as the purified product.

[0032] In another preferred embodiment, step (9) purification comprises the following steps:

[0033] (9-1) Add magnetic beads at a volume ratio of 1-2 times to the library amplification product in step (8), shake to mix, and then remove the supernatant;

[0034] (9-2) Add 6-9 times the volume of the product from step (8) to the product from step (9-1) for washing;

[0035] (9-3) Add 0.25-0.5 times the volume of the product from step (8) to the product from step (9-2) and mix well, so that the magnetic beads are resuspended and allowed to stand.

[0036] (9-4) Collect the supernatant from step (8) with a product volume ratio of 0.3-0.5 as the final product for library construction.

[0037] A third aspect of the present invention provides a high-throughput automated small RNA library, wherein the library is prepared using the method of the first aspect of the present invention.

[0038] The fourth aspect of the present invention provides the use of the high-throughput automated small RNA library described in the third aspect of the present invention as a library for distinguishing the intrinsic biological differences in small RNA expression levels among different plasma samples.

[0039] In another preferred embodiment of the four aspects mentioned above, the 3' connector connection step is performed in step (2), and the incubation time is greater than 10 hours.

[0040] In another preferred embodiment, the incubation environment for step (2) 3' adapter ligation, step (5) ligation of 5' adapter and / or step (6) reverse transcription is any one of, but not limited to, a temperature-controlled and stable dry thermostat, a temperature circulator, or a thermal circulator.

[0041] In another preferred embodiment, the 3'4N adenylated random adapter used in step (2) for 3' adapter ligation is diluted 3-5 times before use, and / or the 5'4N random adapter used in step (5) for ligating 5' adapters is diluted 3-5 times before use.

[0042] In another preferred embodiment, the small RNA includes miRNA, YRNA, tRNA, snRNA, snoRNA, piRNA, etc.

[0043] In another preferred embodiment, the miNRA sample is derived from plasma, serum, or blood.

[0044] In another preferred embodiment, the small RNA fragments in the small RNA library are 18–40 nt in length.

[0045] In another preferred embodiment, the concentration of the small RNA sample in step (1) of the small RNA library is 1-10 ng / uL, more preferably 2-8 ng / uL, and even more preferably 2-5 ng / uL.

[0046] It should be understood that, within the scope of this invention, the above-described technical features of this invention and the technical features specifically described below (such as in the embodiments) can be combined with each other to form new or preferred technical solutions. Due to space limitations, they will not be described in detail here. Attached Figure Description

[0047] Figure 1 The chart shows a comparison of the output of automated and manual document libraries before optimization.

[0048] Figure 2 The results show that the optimized automated library construction method has a better ability to distinguish miRNA expression levels from different biological samples.

[0049] Figure 3 The image shows a heatmap of miRNA expression matrices from different batches of plasma reference material analyzed using an optimized automated library construction method.

[0050] Figure 4 The chart shows a comparison of the output of the automated document library before and after optimization.

[0051] Figure 5 The figures show a comparison of the number of small RNAs detected before and after optimization (Figure A is a comparison of the number of all small RNAs detected before and after optimization, and Figure B is a comparison of the number of miRNAs detected before and after optimization).

[0052] Figure 6 The chart shows a comparison of absolute quantitative consistency before and after optimization.

[0053] Figure 7 The graph shows a comparison of the cross-batch consistency of relative quantification between the two types of biological samples before and after optimization.

[0054] Figure 8 This demonstrates the batch-wise stability of the optimized automated method for miRNA detection.

[0055] Figure 9 This demonstrates the batch-to-batch consistency of evaluating the absolute quantification of miRNA using the optimized automated method.

[0056] Figure 10 This demonstrates the cross-batch consistency of relative quantification between two types of biological samples using an optimized automated method.

[0057] Figure 11 This demonstrates the cross-batch consistency of the optimized automated method for analyzing differential expression between two types of biological samples. Detailed Implementation

[0058] Through extensive and in-depth research, the inventors have developed, for the first time, a high-throughput automated method for constructing plasma small RNA libraries. This invention is the first to discover that optimizing the addition of reagents ADS and Isopropanol, as well as the type of Elution Buffer, in the Excess 3' Adapter Removal step of library construction can increase library yield by 5 times. Changing the workstation incubation in the Excess 3' Adapter Inactivation, 5' Adapter Ligation, and Reverse Transcription steps from workstation incubation to incubation in a PCR instrument can increase library yield by 1.4 times and RNA detection by 1.1 times. Furthermore, removing the magnetic bead selection step can increase library yield by 1.9 times, implying that removing the long-fragment magnetic bead selection step is actually detrimental to plasma samples lacking long-fragment nucleic acid molecules. The optimized method of this invention has good discriminative ability against differences in miRNA expression among different biological samples and can be used for cross-batch stability and quality assessment. This invention was completed based on these findings.

[0059] the term

[0060] In this invention, the term "small RNA" refers to RNA molecules with a length of less than 200 nucleotides. It mainly includes non-coding RNAs such as microRNA (miRNA), small interfering RNA (siRNA), and transfer RNA (tRNA). Among them, those with a length in the range of 18–25 nt are primarily microRNAs (miRNA).

[0061] The main advantages of this invention include:

[0062] (1) This invention optimizes a high-throughput automated plasma library construction method and uses three plasma reference materials. Based on this automated library construction method, 32 small RNA-seq libraries were generated in 4 batches. The performance of the optimized method and its cross-batch stability were evaluated from the aspects of miRNA detection types, absolute quantification, relative quantification, differential expression analysis, etc.

[0063] (2) The four batches of libraries generated by the optimization method of the present invention have good cross-batch stability at the absolute quantitative level.

[0064] (3) The four batches of libraries generated by the optimization method of the present invention do not show obvious batch effects in miRNA detection.

[0065] (4) The optimized method of this invention can stably generate small RNA data from large batches of plasma samples, and can be used in large-scale clinical cohort plasma small RNA library construction experiments. Currently, this method has been applied to the International Human Phenome Project, generating small RNA data from more than 5,000 plasma samples across multiple cohorts, providing strong technical support for biological and basic medical research.

[0066] Example 1: Method for constructing a library

[0067] instrument: NGSx Automated Liquid Workstation

[0068] Plasma reference material:

[0069] This invention prepared three types of plasma reference materials, named P10, P11, and PM, respectively. P10 and P11 were plasma samples from one healthy male volunteer and one healthy female volunteer, respectively, while PM was a mixture of plasma from a diabetic patient. This invention was approved by the Ethics Committee of the School of Life Sciences, Fudan University, and all volunteers participated voluntarily and signed informed consent forms.

[0070] Plasma small RNA extraction:

[0071] Take 200 μL of plasma sample and thaw at 4°C. Use the QIAcube fully automated nucleic acid purification system (QIAGEN) with the miRNeasy Serum / Plasma Advanced Kit (QIAGEN) to complete small RNA extraction according to the standard experimental procedures provided by the manufacturer.

[0072] Automated small RNA library construction:

[0073] use small RNA-seq kit with The company The NGSx automated workstation was used to complete the library construction experiments using the optimized small RNA library construction program. Except for the following operations, all procedures were performed according to the instruction manual: 3' Ligation Adapters and 5' Ligation Adapters were diluted 4-fold; the library was amplified for 25 cycles; the fragment selection process was omitted after amplification (i.e., short fragment enrichment (removal of long fragments) was not performed); all cDNA molecules in the PCR product were completely recovered using magnetic beads; the final library product was obtained.

[0074] The detailed construction method of this invention is described below:

[0075] (1) Extract small RNA from plasma samples;

[0076] (2) The 3' connector connection (3' 4N Adapter Ligation) includes the following steps:

[0077] (2-1) 3' Ligation Master Mix: Dispense the prepared 3' ligation master mix into column 6 of a reagent plate (96-well plate), and then place the reagent tray in the 4-degree temperature control module (B4) of the workstation for later use. The 3' ligation master mix contains: 0.09-0.1 times the sample volume of 3'4N adenylated random ligation adapter, 0.6-0.7 times the sample volume of adapter buffer, and 0.1-0.2 times the sample volume of ligase reagent;

[0078] (2-2) Transfer the extracted product from step (1) to a sample plate and place the sample plate on the 4℃ temperature control module of the workstation (D4). The total volume of the sample and the 3' adapter ligation reagent is 20uL. RNA denaturation is performed at 65-75℃.

[0079] (2-3) Immediately transfer the denatured product to the 4℃ temperature control module of the workstation for cooling;

[0080] (2-4) Remove the sample plate, centrifuge quickly to ensure there are no air bubbles in the sample tube, and incubate at 25°C for more than 10 hours;

[0081] (3) Excess Adapter Removal includes the following steps:

[0082] (3-1) Add 1.05-1.5 times the volume of the adapter removal solution and 2 times the volume of the magnetic beads of the final product of step (2) to the product of step (2-4) and mix.

[0083] (3-2) Add isopropanol in a volume ratio of 3 times that of the final product of step (2) to the product of step (3-1), mix and react for 5 minutes;

[0084] (3-3) After 5 minutes, place the sample plate on the magnetic rack, let it stand for 5 minutes until the solution is clear, and then remove the supernatant;

[0085] (3-4) Place the sample plate back on the working platform, add 140-180 uL of 80% ethanol, react for 30 seconds and then remove the ethanol.

[0086] (3-5) Repeat step (3-4) and let stand for 2-3 minutes to allow the ethanol to evaporate completely;

[0087] (3-6) Add 1-1.2 times the volume of the final product from step (2) to the product from step (3-5), mix by pipetting to resuspend the magnetic beads, and incubate at room temperature for 2 minutes.

[0088] (3-7) Place the sample plate on the magnetic rack and react for 3 minutes to allow the magnetic beads to adhere to the magnetic ring on the tube wall.

[0089] (3-8) After the solution has clarified, collect the supernatant from the product of step (3-7) with a volume ratio of 1 to the final product of step (2) and transfer it to a new sample plate.

[0090] (3-9) Repeat steps (3-1) to (3-7), wherein the buffer in step (3-6) is replaced with nucleic acid-free water at a volume ratio of 0.5-0.7 times that of the final product in step (2);

[0091] (3-10) After the solution has clarified, collect the supernatant into a new sample plate (D4);

[0092] (4) Excess Adapter Inactivation includes the following steps:

[0093] (4-1) Prepare 3' adapter inactivation mix and dispense it into column 5 of the reagent plate. Then place the reagent plate in the 4-degree temperature control module (B4) of the workstation for later use. The reagent contains: adapter inactivation buffer at a volume ratio of 0.2-0.25 times that of the final product in step (3) and adapter inactivation enzyme at a volume ratio of 0.04-0.045 times that of the final product in step (3).

[0094] (4-2) After mixing the final product with the 3' connector deactivation reagent in step (3), remove the sample plate from the workstation and centrifuge to ensure that all samples are free of air bubbles;

[0095] (4-3) Place the sample plate in the PCR instrument and incubate it under the following conditions: react at 10-15℃ for 10-20 minutes, react at 45-65℃ for 15-25 minutes, and finally stop at 4℃.

[0096] (5) The 5' connector connection (5' 4N Adapter Ligation) includes the following steps:

[0097] (5-1) Prepare the 5' adapter ligation master mix. Dispense the prepared 5' adapter ligation master mix into column 4 of the reagent plate. Then place the reagent plate in the 4°C temperature control module (B4) of the workstation for later use. The 5' adapter ligation master mix contains:

[0098] 0.09-0.11 times the sample volume of 5'4N random adapter reagent, 0.5-0.6 times the sample volume of adapter buffer, and 0.1-0.2 times the sample volume of ligase reagent;

[0099] (5-2) Take out the sample plate after the incubation in step (4-3), place it in position D4 of the automated workstation, and mix in the 5' connector connection reagent;

[0100] (5-3) Remove the sample plate, centrifuge quickly to ensure there are no air bubbles in the sample, and then place it at 17-23℃ for 1 hour;

[0101] (6) Reverse transcription includes the following steps: add reverse transcription solution at a volume ratio of 0.6 times that of the product in step (5-3) to the product in step (5-3), carry out the reaction in an adjustable thermostat, and proceed directly to the next step after the reaction;

[0102] (7) The first magnetic bead purification process includes the following steps:

[0103] (7-1) Add 0.2-0.3 times the volume of adapter depletion solution to the final product of step (6);

[0104] (7-2) Add magnetic beads at a volume ratio of 1 to the product of step (7-1) of the final product of step (6) and mix.

[0105] (7-3) Add isopropanol in a volume ratio of 2-2.5 times to the product of step (6) and mix;

[0106] (7-4) Incubate the product from step (7-3) at room temperature for 5 minutes. After 5 minutes, transfer the sample to a magnetic rack and let it stand until the supernatant becomes clear. Then remove the supernatant.

[0107] (7-5) Ethanol washing: Add 160-180 μL of 80% ethanol, incubate for 30 seconds, and remove all supernatant. Repeat this step twice.

[0108] (7-6) Place the sample on the magnetic rack for 3 minutes to dry the liquid remaining on the magnetic beads, remove all the residual liquid collected at the bottom of the well, remove the sample from the magnetic rack, add 0.625 times the volume of the final product from step (6) to the sample, mix well and incubate for 2 minutes.

[0109] (7-7) Place the sample on a magnetic rack and let it stand until the supernatant becomes clear. Transfer the supernatant to a new sample plate.

[0110] (8) PCR amplification includes the following steps:

[0111] (8-1) Prepare PCR reaction reagents.

[0112] The PCR reaction reagents include universal primers, barcode primers, and PCR master mix; wherein the volume ratio of the aforementioned substances is universal primers: barcode primers: PCR master mix = 1:1:5.

[0113] (8-2) Add 7.5 μL of PCR reaction reagent to the product of step (7-7), and the total volume after mixing is 30 μL;

[0114] (9) The second magnetic bead purification process includes the following steps:

[0115] (9-1) Add magnetic beads with a volume ratio of 1.7-2 times that of the final product in step (8) to the product in step (8-2), and shake for 5 minutes to mix.

[0116] (9-2) Place the product from step (9-1) on a magnetic rack and let it stand at room temperature for 5 minutes. After removing the supernatant, add 140-180 uL of 80% ethanol to wash. After 30 seconds at room temperature, discard the supernatant. Repeat this step once.

[0117] (9-3) Let the sample stand on the magnetic rack for another 3 minutes to allow the residual alcohol to evaporate;

[0118] (9-4) Add 0.4-0.5 times the volume of the final product from step (8) to step (9-3), shake to mix for 2 minutes, place the sample on a magnetic rack for 2 minutes, and remove the magnetic beads;

[0119] (9-5) Add 0.3-0.5 times the volume of the final product from step (8) to the product from step (9-4) to obtain the final product for library construction.

[0120] In a preferred embodiment, the incubation environment of steps (2-4), (5-3) and / or step (6) includes, but is not limited to, a temperature-controlled and stable dry thermostat, a temperature circulator, and a heat circulator.

[0121] The 3'4N adenosine-modified random adapter used in step (1) for 3' adapter ligation needs to be diluted 3-5 times before use, and / or the 5'4N random adapter used in step (4) for ligating 5' adapters needs to be diluted 3-5 times before use.

[0122] Example 2: Performance Analysis Method of Database Construction Results

[0123] Small RNA library quality control and sequencing:

[0124] Using a Qubit fluorometer ( 3.0 Fluorometer) The dsDNA HS Assay Kits were used to measure the concentration of each library in Example 1 and calculate the library yield. Library sequencing experiments were performed at Mingma Biotechnology using Illumina Hiseq platform with paired-end 150bp sequencing. Sequencing results were the raw sequences of each read, stored in FastQ format.

[0125] Sequence alignment, counting, and normalization:

[0126] This invention uses the exceRpt (The extracellular RNA processing toolkit) data preprocessing workflow to perform sequence alignment and counting on the sequencing data of each library. This workflow can remove the 3' adapter sequence based on a given sequence, filter out low-quality reads, and remove sequences aligned to ribosomal RNA and exogenous contaminant RNA (NCBI UniVec) from the sample. Then, the reads are aligned to the Human Reference Genome (Human reference genome build GenomeReference Consortium GRCh38, UCSC hg38) and miRBase version 21 (…). http: / / www.mirbase.org / ), count the number of miRNA reads in each sequencing file.

[0127] Based on the original read counts, this invention uses the CPM (Count Per Million) method to standardize the original expression level of each library, CPM = (count + 1) / (total small RNA reads), and then performs log2 transformation. This yields the log2 CPM expression profile for subsequent analysis.

[0128] Method for calculating repeatability:

[0129] The Jaccard Index is used to measure the consistency of miRNA detection between two samples (differential detection). Also known as the Intersection over Union ratio, the Jaccard Index is the ratio of the intersection of the miRNA detection sets of two samples to the union of their respective sizes. The formula is as follows:

[0130]

[0131] Where A and B represent the miRNA detection sets of the two samples, respectively. The Jaccard Index ranges from [0,1]. The closer the Jaccard Index is to 1, the higher the consistency of miRNA detection between the two samples; conversely, the closer it is to 0, the lower the consistency of detection between the two samples.

[0132] Example 3: Establishment and performance of an automated plasma small RNA library construction method.

[0133] First, to evaluate the quality of small RNA-seq libraries generated by the automated library preparation workstation, this invention used both manual operation and the manufacturer's original automated method to perform parallel library preparation on small RNAs purified from three types of plasma reference materials (P10, P11, and PM), generating a total of 87 small RNA-seq libraries. We refer to the 74 libraries generated by the automated workstation and the 13 libraries generated manually as automated libraries and manual libraries, respectively, and categorize them based on library yield (…). Figure 1 and the degree of differentiation between different biological samples () Figure 2 The quality of the two types of libraries was evaluated from these two perspectives. The evaluation results showed that, on the one hand, the yield of automated libraries was extremely low (8.5±5.6 ng), only 8% of the yield of manually constructed libraries (105.6±66.2 ng). On the other hand, principal component analysis of 13 manually constructed libraries revealed that libraries from the same plasma sample clustered together, while libraries from different samples were clearly separated. Figure 2 A). However, the automated libraries provided by the manufacturer cannot distinguish between libraries from the same or different plasma samples. Figure 2B) This means that the library sequencing data obtained using automated workstations is too noisy, even exceeding the inherent biological differences between different samples. All of the above evidence indicates that the original commercial automated library preparation programs provided by manufacturers cannot accurately simulate manual small RNA-seq library preparation, resulting in low-quality small RNA libraries that fail to meet requirements.

[0134] To address this issue, we optimized key factors affecting library quality and constructed four batches (numbered a, b, c, and d) of plasma reference material small RNA libraries using the optimized automated library construction method, with eight samples per batch (three P10, three P11, and two PM samples). Compared to the original automated program provided by the manufacturer, the optimized automated program increased library yield by nearly four times. Figure 4 Principal component analysis and unsupervised clustering results showed that optimized technical replicates from different batches of the same plasma sample preferentially clustered together. Figure 2 C Figure 3 The libraries from different plasma samples were clearly separated, indicating that the optimized automated method has a good ability to distinguish the differences in miRNA expression among different biological samples.

[0135] Example 4: Performance Comparison of Automated Database Building Methods Before and After Optimization

[0136] This embodiment compares the library product concentration, small RNA detection count, and absolute quantification consistency of miRNA in a single biological sample, as well as the relative quantification consistency between two types of biological samples, before and after optimization of the automated library construction system. Results are derived from... Figure 8-10 It can be seen that, after optimization by this invention, the library concentration ( Figure 4 The library concentration before optimization was 0.71±0.46 ng / uL, and the concentration after optimization was 2.80±1.38 ng / uL. The concentration after optimization was significantly higher than that before optimization (P=8.94e-10). Small RNA detection count ( Figure 5 Small RNA detection count: 1.93-fold increase after optimization compared to before optimization (P = 6.33e-16) Figure 5 A); miRNA detection count: 1.49-fold increase after optimization compared to before optimization (P = 4.17e-14) Figure 5 B). Reproducibility of absolute miRNA quantification ( Figure 6 The optimization showed a 1.14-fold improvement compared to the unoptimized result (P = 5.28e-103). The relative quantitative consistency of miRNAs between the two types of biological samples was significantly higher after optimization than before (P = 8.10e-5). Figure 7 ).

[0137] Example 5: Evaluation of cross-batch stability of small RNA detection using the optimized automated method

[0138] The stability of miRNA detection in similar samples is a prerequisite for reliable quantitative results. Therefore, we analyzed cross-batch miRNA detection from two aspects: the number of miRNAs detected and the types of miRNAs detected. Figure 8 A) Stability is assessed.

[0139] Basic information on the types of miRNA detected in the three types of plasma samples is as follows: Figure 8 As shown in B, the number of miRNAs detected in different batches of the library remained relatively stable, approximately 300–400. Specifically, the number of miRNAs detected in samples P10, P11, and PM were 337±44, 380±54, and 322±25 (mean±sd), respectively.

[0140] To assess the consistency of miRNA detection across batches, we used the Jaccard Index to quantitatively describe the similarity of miRNA species detected in two experiments. This was achieved by calculating the similarity between any two pairs of samples. The crossover ratio of detected species in each comparison, for inter-batch comparisons. Figure 8 A) The crossover ratio between technical repetitions and the ratio between different technical repetitions in the same batch (intra-batch, Figure 8 A) was compared with the crossover ratio. First, the detection consistency between different technical replicates of the same type of plasma sample was significantly higher than the detection consistency between different plasma samples (A). Figure 8 C) indicates that the types of miRNAs expressed in different plasma samples vary; for the same plasma reference sample, there is no statistically significant difference in the Jaccard Index between cross-batch technical replicates and between technical replicates within the same batch. Figure 8 The D and t-test p-values ​​were 0.41, 0.51 and 0.68, respectively, indicating that the four batches of libraries generated in this invention did not show a significant batch effect in miRNA detection.

[0141] Example 6: Evaluation of batch-to-batch stability of the optimized automated method for absolute quantification of small RNA

[0142] Consistency of absolute expression levels across technical replicates is a prerequisite for quantitative reliability. To assess the cross-batch stability of miRNA absolute quantification, we used the Pearson correlation coefficient of miRNA expression vectors between two samples as an indicator of the consistency of absolute miRNA detection quantification between the two samples. For each plasma sample, we performed a t-test on the absolute quantification correlation coefficients between inter-batch and intra-batch samples. Figure 9 A). For samples P10 (P = 0.26) and P11 (P = 0.24), there was no significant difference between batch consistency and intra-batch consistency. However, for PM (P = 0.016), the intra-batch correlation coefficient was slightly higher than the inter-batch correlation coefficient. This may be related to the smaller number of technical replicates in the PM sample. Overall, there was no statistically significant difference in consistency between different technical replicates within the same batch and between technical replicates across different batches (P = 0.8).

[0143] Furthermore, we calculated the pairwise correlation coefficient matrix for all samples and performed unsupervised hierarchical clustering based on this matrix. The clustering results showed that samples from the same plasma source clustered preferentially. Figure 9 (B) Samples from the same batch showed no significant clustering. This indicates that batch-to-batch quantification differences are random and less than the inherent differences between different biological samples. These results demonstrate that the four batches of libraries generated in this invention exhibit good cross-batch stability at the absolute quantification level.

[0144] Example 7: Evaluation of the cross-batch stability of relative quantification between two samples using the optimized automated method

[0145] Absolute quantitative consistency only indicates that a method has good measurement reproducibility for a single type of biological sample, while biomarker screening often relies on the stable and reliable detection of differences in miRNA expression levels between different biological sample groups. Therefore, we evaluated the reproducibility of relative miRNA quantification between two groups of biological samples.

[0146] Therefore, we used the limma software package to perform differential expression analysis on P10 and P11 samples (P10 / P11) of three batches of libraries (differential expression analysis was performed on three technical replicates of P10 samples and three technical replicates of P11 samples within each batch, for a total of three differential expression analyses). The resulting log2FC vectors represent the relative expression levels between the two samples in that batch (this analysis only used libraries from batches a, c, and d; batch b was not included in this analysis because it only had two P10 samples). We used the Pearson correlation coefficient between the log2FC vectors of the two batches to measure the relative consistency between batches. The pairwise correlation coefficients between the three batches were 0.69, 0.75, and 0.79, respectively. Figure 10 The results indicate that the relative quantitative consistency between batches of samples P10 / P11 is good.

[0147] Example 8: Evaluating the cross-batch stability of differential expression analysis between two samples

[0148] Furthermore, to evaluate the reliability of this method in detecting differentially expressed miRNAs between groups, we assessed the consistency of differential expression results between batches. The limma package was used to identify differentially expressed miRNAs between samples P10 and P11 of each batch (P < 0.05, and |log2FC| > log2(1.5)).

[0149] 63, 68, and 61 differentially expressed miRNAs were detected in each batch, respectively. Figure 11 A) A total of 44 upregulated miRNAs were detected, of which 27 could be detected in at least two batches (61.3%); a total of 49 downregulated miRNAs were detected, of which 33 (67.3%) could be detected in at least two batches. Figure 11 B and C) indicate that the results of cross-batch differential expression analysis among biological samples have good consistency.

[0150] discuss

[0151] This invention uses the same reference sample set to conduct four consecutive batches of library construction experiments, with 2-3 technical replicates designed for the same type of plasma sample within each batch. Intra-batch and inter-batch technical replicates are designed, and inter-batch consistency is evaluated based on intra-batch technical replicate consistency. This demonstrates that the optimized high-throughput automated library construction method of this invention not only increases product yield but also exhibits high stability across batches of samples. It has reference value for quality assessment of large-scale cohort data generation platforms and can be used in large-scale clinical cohort plasma small RNA library construction experiments.

[0152] It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Experimental methods not specifically described in the embodiments are generally performed under conventional conditions as described in Sambrook et al., Molecular Cloning: A Laboratory Manual (New York: Cold Spring Harbor Laboratory Press, 1989) or Plant Molecular Biology: A Laboratory Manual (edited by Melody S. Clark, Springer-Verlag Berlin-Heidelberg, 1997), or as recommended by the manufacturer. Unless otherwise stated, percentages and parts are by weight.

[0153] All documents mentioned in this invention are incorporated herein by reference as if each document were individually incorporated by reference. Furthermore, it should be understood that after reading the foregoing teachings of this invention, those skilled in the art can make various alterations or modifications to this invention, and these equivalent forms also fall within the scope defined by the appended claims.

Claims

1. A high-throughput automated small RNA library construction method using the NEXTFLEX ® small RNA-seq kit for library construction, and the method uses the instrument Sciclone ® NGSx automated liquid workstation, characterized in that, Including the following steps: (1) Provide isolated small RNA samples; (2) 3' adapter ligation is performed on the small RNA in the small RNA sample to obtain a 3' adapter-containing sample; (3) Remove excess 3' connectors from the 3' samples with connectors; (4) Deactivate the excess 3' connector; (5) Connect the sample obtained in step (4) with a 5' connector to obtain a sample with a 5' connector; (6) Reverse transcribe the sample obtained in step (5) to obtain a cDNA sample with adapters at both ends; (7) Purify the sample obtained in step (6); (8) Perform library amplification on the purified sample obtained in step (7) to obtain amplification products; (9) The amplification product is purified and a library is constructed to obtain a small RNA library; The removal of the excess 3' connector in the Excess Adapter Removal process includes the following steps: (3-1) Add 1.05-1.5 times the volume of the adapter removal reagent and 2 times the volume of the magnetic beads of the final product of step (2) to the product of step (2) and mix. (3-2) Add isopropanol in a volume ratio of 3 times that of the final product in step (2) to the product in step (3-1), mix and react for 5 minutes; (3-3) After 5 minutes, place the sample plate on the magnetic rack and let it stand for 5 minutes until the solution becomes clear, then remove the supernatant; (3-4) Place the sample plate back on the working platform, add 140-180 uL of 80% ethanol, react for 30 seconds and then remove the ethanol; (3-5) Repeat step (3-4) and let stand for 2-3 minutes to allow the ethanol to evaporate completely; (3-6) Add 1-1.2 times the volume of the final product from step (2) to the product from step (3-5), mix by pipetting to resuspend the magnetic beads, and incubate at room temperature for 2 minutes. (3-7) Place the sample plate on the magnetic rack and react for 3 minutes to allow the magnetic beads to adhere to the magnetic ring on the tube wall. (3-8) After the solution has clarified, collect the supernatant from the product of step (3-7) with a volume ratio of 1 to the final product of step (2) and transfer it to a new sample plate; (3-9) Repeat steps (3-1) to (3-7), wherein the buffer in step (3-6) is replaced with nucleic acid-free water at a volume ratio of 0.5-0.7 times that of the final product in step (2); (3-10) After the solution has clarified, collect the supernatant into a new sample plate; The 3' 4N Adenylated Adapter used in step (2) for 3' adapter connection needs to be diluted 3-5 times before use, and / or the 5' 4N Adenylated Adapter used in step (5) for 5' adapter connection needs to be diluted 3-5 times before use.

2. The method of claim 1, wherein, In step (2), the 3' connector connection step is performed, and the incubation time is greater than 10 hours.

3. The method of claim 1, wherein, The incubation environment for step (2) 3' adapter ligation, step (4) deactivation of excess 3' adapters, step (5) ligation of 5' adapters and / or step (6) reverse transcription is any one of a temperature-controlled and stable dry thermostat, temperature circulator, or thermal circulator.

4. The method of claim 1, wherein, The purification step (9) includes the following steps: (9-1) Add magnetic beads at a volume ratio of 1-2 times to the library amplification product in step (8), shake to mix, and then remove the supernatant; (9-2) Add 6-9 times the volume of the product from step (8) to the product from step (9-1) for washing; (9-3) Add 0.25-0.5 times the volume of the product from step (8) to the product from step (9-2) and mix well, so that the magnetic beads are resuspended and allowed to stand. (9-4) Collect the supernatant from step (8) with a product volume ratio of 0.3-0.5 as the final product for library construction.

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