SEAOP: a statistical ensemble approach for outlier detection in quantitative proteomics data.

Quality control in quantitative proteomics is a persistent challenge, particularly in identifying and managing outliers. Unsupervised learning models, which rely on data structure rather than predefined labels, offer potential solutions. However, without clear labels, their effectiveness might be compromised. Single models are susceptible to the randomness of parameters and initialization, which can result in a high rate of false positives. Ensemble models, on the other hand, have shown capabilities in effectively mitigating the impacts of such randomness and assisting in accurately detecting true outliers. Therefore, we introduced SEAOP, a Python toolbox that utilizes an ensemble mechanism by integrating multi-round data management and a statistics-based decision pipeline with multiple models. Specifically, SEAOP uses multi-round resampling to create diverse sub-data spaces and employs outlier detection methods to identify candidate outliers in each space. Candidates are then aggregated as confirmed outliers via a chi-square test, adhering to a 95% confidence level, to ensure the precision of the unsupervised approaches. Additionally, SEAOP introduces a visualization strategy, specifically designed to intuitively and effectively display the distribution of both outlier and non-outlier samples. Optimal hyperparameter models of SEAOP for outlier detection were identified by using a gradient-simulated standard dataset and Mann-Kendall trend test. The performance of the SEAOP toolbox was evaluated using three experimental datasets, confirming its reliability and accuracy in handling quantitative proteomics.

Suppression of pinoid mutant phenotypes by mutations in PIN-FORMED 1 and PIN1-GFP fusion.

Disruption of either the auxin transporter PIN-FORMED 1 (PIN1) or the protein kinase PINOID (PID) leads to the development of pin-like inflorescences. Previous studies have shown that phosphoregulation of PIN1 by AGC kinases including PID directs auxin flux to drive organ initiation. Here, we report unexpected findings on the genetic interactions between these two genes. We deleted the first 2/3 of the PIN1 coding sequence using CRISPR/Cas9, and the resulting pin1 mutant (pin1-27) was a strong allele. Surprisingly, heterozygous pin1-27 suppressed two independent pid null mutants, whereas homozygous pin1-27 enhanced the phenotypes of the pid mutants during embryogenesis. Furthermore, we show that deletion of either the hydrophilic loop or the second half of PIN1 also abolished PIN1 function, yet those heterozygous pin1 mutants were also capable of rescuing pid nulls. Moreover, we inserted green fluorescent protein (GFP) into the hydrophilic loop of PIN1 through CRISPR-mediated homology-directed repair (HDR). The GFP signal and pattern in the PIN1-GFPHDR line are similar to those in the previously reported PIN1-GFP transgenic lines. Interestingly, the PIN1-GFPHDR line also rescued various pid null mutant alleles in a semidominant fashion. We conclude that decreasing the number of functional PIN1 copies is sufficient to suppress the pid mutant phenotype, suggesting that PIN1 is likely part of a larger protein complex required for organogenesis.

Nanoparticle Deep-Subwavelength Dynamics Empowered by Optical Meron-Antimeron Topology.

Optical meron is a type of nonplanar topological texture mainly observed in surface plasmon polaritons and highly symmetric points of photonic crystals in the reciprocal space. Here, we report Poynting-vector merons formed at the real space of a photonic crystal for a Γ-point illumination. Optical merons can be utilized for subwavelength-resolution manipulation of nanoparticles, resembling a topological Hall effect on electrons via magnetic merons. In particular, staggered merons and antimerons impose strong radiation pressure on large gold nanoparticles (AuNPs), while focused hot spots in antimerons generate dominant optical gradient forces on small AuNPs. Synergistically, differently sized AuNPs in a still environment can be trapped or orbit in opposite directions, mimicking a coupled galaxy system. They can also be separated with a 10 nm precision when applying a flow velocity of >1 mm/s. Our study unravels a novel way to exploit topological textures for optical manipulation with deep-subwavelength precision and switchable topology in a lossless environment.

Programmable Framework Nucleic Acid-Modified Nanomagnetic Beads for Efficient Isolation of Exosomes and Exosomal Proteomics Analysis.

Exosomes are increasingly being regarded as emerging and promising biomarkers for cancer screening, diagnosis, and therapy. The downstream molecular analyses of exosomes were greatly affected by the isolation efficiency from biosamples. Among the current exosome isolation strategies, affinity nanomaterials performed comparably better with selectivity and specificity. However, these techniques did not take the structure and size of exosomes into account, which may lead to a loss of isolation efficiency. In this article, a framework nucleic acid was employed to prepare a well-designed nanosized bead Fe3O4@pGMA@DNA TET@Ti4+ for enrichment of exosomes. The abundant phosphate groups in the framework nucleic acid provide binding sites to immobilize Ti4+, and its rigid three-dimensional skeleton makes them act as roadblocks to barricade exosomes and provide affinity interactions on a three-dimensional scale, resulting in the improvement of isolation efficiency. The model exosomes can be effectively isolated with 92% recovery in 5 min. From 100 µL of HeLa cell culture supernatant, 34 proteins out of the top 100 commonly identified exosomal proteins were identified from the isolated exosomes by the novel beads, which is obviously more than that by TiO2 (19 proteins), indicating higher isolation efficiency and exosome purity by Fe3O4@pGMA@DNA TET@Ti4+ beads. The nanobeads were finally applied for comparing exosomal proteomics analysis from real clinical serum samples. Twenty-five upregulated and 10 downregulated proteins were identified in the lung cancer patients group compared to the health donors group, indicating that the novel nanobeads have great potential in isolation of exosomes for exosomal proteomics analysis in cancer screening and diagnosis.

Machine learning and deep learning enabled age estimation on medial clavicle CT images.

The medial clavicle epiphysis is a crucial indicator for bone age estimation (BAE) after hand maturation. This study aimed to develop machine learning (ML) and deep learning (DL) models for BAE based on medial clavicle CT images and evaluate the performance on normal and variant clavicles. This study retrospectively collected 1049 patients (mean± SD: 22.50±4.34 years) and split them into normal training and test sets, and variant training and test sets. An additional 53 variant clavicles were incorporated into the variant test set. The development stages of normal MCE were used to build a linear model and support vector machine (SVM) for BAE. The CT slices of MCE were automatically segmented and used to train DL models for automated BAE. Comparisons were performed by linear versus ML versus DL, and normal versus variant clavicles. Mean absolute error (MAE) and classification accuracy was the primary parameter of comparison. For BAE, the SVM had the best MAE of 1.73 years, followed by the commonly-used CNNs (1.77-1.93 years), the linear model (1.94 years), and the hybrid neural network CoAt Net (2.01 years). In DL models, SE Net 18 was the best-performing DL model with similar results to SVM in the normal test set and achieved an MAE of 2.08 years in the external variant test. For age classification, all the models exhibit superior performance in the classification of 18-, 20-, 21-, and 22-year thresholds with limited value in the 16-year threshold. Both ML and DL models produce desirable performance in BAE based on medial clavicle CT.

Bone age assessment based on different MRI modalities of the proximal humerus epiphysis: the comparisons of T1WI, T2WI, and PDWI.

Bone age assessment (BAA) is crucial in various fields, including legal proceedings, athletic competitions, and clinical medicine. However, the use of X-ray methods for age estimation without medical indication is subject to ethical debate, especially in forensic and athletic fields. The application of magnetic resonance imaging (MRI) with non-ionizing radiation can overcome this limitation in BAA. This study aimed to compare the application value of several MRI modalities of proximal humeral in BAA. A total of 468 patients with shoulder MRIs were retrospectively collected from a Chinese Han population aged 12-30 years (259 males and 209 females) for training and testing, including T1 weighted MRI (T1WI), T2 weighted MRI (T2WI), and Proton density weighted MRI (PDWI). Optimal regression models were established for age estimation, yielding mean absolute error (MAE) values below 2.0 years. The MAE values of T1WI were the lowest, with 1.700 years in males and 1.798 years in females. The area under the curve (AUC) and accuracy values of different MRI modalities of 16-year and 18-year thresholds were all around 0.9. For the 18-year threshold, T1WI outperformed T2WI and PDWI. In conclusion, the three MRI modalities of the proximal humerus can serve as reliable indicators for age assessment, while the T1WI performed better in age assessment and classification.

Automated bone age assessment from knee joint by integrating deep learning and MRI-based radiomics.

Bone age assessment (BAA) is a crucial task in clinical, forensic, and athletic fields. Since traditional age estimation methods are suffered from potential radiation damage, this study aimed to develop and evaluate a deep learning radiomics method based on multiparametric knee MRI for noninvasive and automatic BAA. This retrospective study enrolled 598 patients (age range,10.00-29.99 years) who underwent MR examinations of the knee joint (T1/T2*/PD-weighted imaging). Three-dimensional convolutional neural networks (3D CNNs) were trained to extract and fuse multimodal and multiscale MRI radiomic features for age estimation and compared to traditional machine learning models based on hand-crafted features. The age estimation error was greater in individuals aged 25-30 years; thus, this method may not be suitable for individuals over 25 years old. In the test set aged 10-25 years (n = 95), the 3D CNN (a fusion of T1WI, T2*WI, and PDWI) demonstrated the lowest mean absolute error of 1.32 ± 1.01 years, which is higher than that of other MRI modalities and the hand-crafted models. In the classification for 12-, 14-, 16-, and 18- year thresholds, accuracies and the areas under the ROC curves were all over 0.91 and 0.96, which is similar to the manual methods. Visualization of important features showed that 3D CNN estimated age by focusing on the epiphyseal plates. The deep learning radiomics method enables non-invasive and automated BAA from multimodal knee MR images. The use of 3D CNN and MRI-based radiomics has the potential to assist radiologists or medicolegists in age estimation.

Establishment of a multi-line immunochromatography based on magnetic nanoparticles for simultaneous screening of multiple biomarkers.

Biomarkers screening is a benefit approach for early diagnosis of major diseases. In this study, magnetic nanoparticles (MNPs) have been utilized as labels to establish a multi-line immunochromatography (MNP-MLIC) for simultaneous detection of carcinoembryonic antigen (CEA), carbohydrate antigen 199 (CA 19-9), and alpha-fetoprotein (AFP) in a single serum sample. Under the optimal parameters, the three biomarkers can be rapidly and simultaneously qualitative screening within 15 min by naked eye. As for quantitative detection, the MNP-MLIC test strips were precisely positioned and captured by a smartphone, and signals on the test and control lines were extracted by ImageJ software. The signal ratio of test and control lines has been calculated and used to plot quantitative standard curves with the logarithmic concentration, of which the correlation coefficients are more than 0.99, and the limit of detection for CEA, CA 19-9, and AFP were 0.60 ng/mL, 1.21 U/mL, and 0.93 ng/mL, respectively. The recoveries of blank serum were 75.0 ~ 112.5% with the relative standard deviation ranging from 2.5 to 15.3%, and the specificity investigation demonstrated that the MNP-MLIC is highly specific to the three biomarkers. In conclusion, the developed MNP-MLIC offers a rapid, simple, accurate, and highly specific method for simultaneously detecting multiple biomarkers in serum samples, which provides an efficient and accurate approach for the early diagnosis of diseases.

Simultaneous determination of diquat, paraquat, glufosinate, and glyphosate in plasma by liquid chromatography/tandem mass spectrometry: from method development to clinical application.

Diquat (DQ), paraquat (PQ), glufosinate (GLU), and glyphosate (GLYP) are commonly used herbicides that have been confirmed to be toxic to humans. Rapid and accurate measurements of these toxicants in clinical practice are beneficial for the correct diagnosis and timely treatment of herbicide-poisoned patients. The present study aimed to establish an efficient, convenient, and reliable method to achieve the simultaneous quantification of DQ, PQ, GLU, and GLYP in human plasma using liquid chromatography-tandem mass spectrometry (LC-MS/MS) without using derivatization or ion-pairing reagents. DQ, PQ, GLU, and GLYP were extracted by the rapid protein precipitation and liquid-liquid extraction method and then separated and detected by LC-MS/MS. Subsequently, linearity, limit of detection (LOD), limit of quantification (LOQ), precision, accuracy, extraction recovery, matrix effect, dilution integrity, and stability were evaluated to validate the method based on the FDA criteria. Finally, the validated method was applied to real plasma samples collected from 166 Chinese patients with herbicide poisoning. The results showed satisfactory linearity with low LOD (1 ng/mL for DQ and PQ, 5 ng/mL for GLU, and 10 ng/mL for GLYP, respectively) and low LOQ (5 ng/mL for DQ and PQ, 25 ng/mL for GLU and GLYP, respectively). In addition, the precision, accuracy, extraction recovery, and stability of the method were acceptable. The matrix effect was not observed in the analyzed samples. Moreover, the developed method was successfully applied to determine the target compounds in real plasma samples. These data provided reliable evidence for the application of this LC-MS/MS method for clinical poisoning detection.

Development of cancer biomarker heat shock protein 90α certified reference material using two different isotope dilution mass spectrometry techniques.

Heat shock protein 90α (HSP90α) has been regarded as an important indicator for judging tumor metastasis and prognosis due to its significant upregulation in various tumors. Therefore, the accurate quantification of HSP90α is of great significance for clinical diagnosis and therapy of cancers. However, the lack of HSP90α certified reference material (CRM) leads to the accuracy and consistency of quantification methods not being effectively evaluated. Besides, quantitative results without traceability make comparisons between different studies difficult. In this study, an HSP90α solution CRM was developed from the recombinant protein raw material. The recombinant protein is a dimer, and the purity of the CRM candidate reached 96.71%. Both amino acid analysis-isotope dilution mass spectrometry (AAA-IDMS) and unique peptide analysis-isotope dilution mass spectrometry (UPA-IDMS) were performed to measure the content of HSP90α in the solution CRM candidate, and the certified value was assessed to be 66.2 ± 8.8 µg/g. Good homogeneity of the CRM was identified, and the stability examination suggested that the CRM was stable for at least 4 months at - 80 °C and for 7 days at 4 °C. With traceability to SI unit (kg), this CRM has potential to help establish a metrological traceability chain for quantification of HSP90α, which will make the quantification results standardized and comparable regardless of the quantitative methods.

Reliable biological and multi-omics research through biometrology.

Metrology is the science of measurement and its applications, whereas biometrology is the science of biological measurement and its applications. Biometrology aims to achieve accuracy and consistency of biological measurements by focusing on the development of metrological traceability, biological reference measurement procedures, and reference materials. Irreproducibility of biological and multi-omics research results from different laboratories, platforms, and analysis methods is hampering the translation of research into clinical uses and can often be attributed to the lack of biologists' attention to the general principles of metrology. In this paper, the progresses of biometrology including metrology on nucleic acid, protein, and cell measurements and its impacts on the improvement of reliability and comparability in biological research are reviewed. Challenges in obtaining more reliable biological and multi-omics measurements due to the lack of primary reference measurement procedures and new standards for biological reference materials faced by biometrology are discussed. In the future, in addition to establishing reliable reference measurement procedures, developing reference materials from single or multiple parameters to multi-omics scale should be emphasized. Thinking in way of biometrology is warranted for facilitating the translation of high-throughput omics research into clinical practices.

The N+ Formation Mechanism of Vibrationally Selected N2O+ Ions in the C2Σ+ State: A TPEPICO Imaging Study.

The N-NO bond fission of N2O+(C2Σ+) ions can produce two major fragment ions, NO+ or N+. In contrast to the dominant NO+ fragment ion, the N+ formation mechanism remains unclear to date. Here, dissociative photoionization of N2O via the C2Σ+ ionic state has been reinvestigated using a combined approach of threshold photoelectron-photoion coincidence (TPEPICO) velocity imaging and quantum chemical calculations. Accompanying the N+(3P) formation, the NO(X2Π) neutral fragment with low and high vi-rotational distributions was identified, based on the N+ speed and angular distributions derived from the TPEPICO images. In particular, the excitation of the symmetric stretching ν1+ mode promotes the formation of high rotational components, while the asymmetric stretching ν3+ mode shows the exact opposite effect. According to our calculated multistate potential energy surfaces, intersystem crossing from C2Σ+ to 14Π exclusively provides feasible decomposition pathways to produce the N+ fragment. In a slightly bent geometry, spin-orbit couplings between C2Σ+ and two substates of 14Π, 14A' or 14A″, play a crucial role in the N+ formation from vibrationally selected N2O+(C2Σ+) ions. The mechanism also provides new insights into the charge transfer reaction of N+ + NO → N + NO+.

Investigation of physiological roles of UDP-glycosyltransferase UGT76F2 in auxin homeostasis through the TAA-YUCCA auxin biosynthesis pathway.

Cellular auxin (indole-3-acetic acid, IAA) levels are coordinately regulated by IAA biosynthesis and inactivation. IAA is synthesized through sequential reactions by two enzymes, TAA1 and YUCCA, in a linear indole-3-pyruvic acid (IPA) pathway. TAA1 converts tryptophan to IPA, and YUCCA catalyzes the oxidative decarboxylation of IPA into IAA. Arabidopsis UDP-glycosyltransferase UGT76F2 (At3g55710) was previously reported to catalyze the glycosylation of IPA and consequently modulate IAA levels. We carefully analyzed the physiological roles of UGT76F2 and its close homolog UGT76F1 (At3g55700) in IAA homeostasis. We generated two independent ugt76f1 ugt76f2 double null Arabidopsis mutants (ugt76f1f2) with a 2.7 kb deletion, along with two independent ugt76f2 single null mutants by CRISPR/Cas9 gene editing technology. Surprisingly, these null mutants exhibited indistinguishable phenotypes from the wild-type seedlings under our laboratory conditions. Our results indicate that UGT76F1 and UGT76F2 do not play important roles in regulating IAA biosynthesis via the IPA glycosylation.

Evolution of Mass Spectrometry Instruments and Techniques for Blood Proteomics.

Mass spectrometry (MS)-based blood proteomics is a crucial research focus in identifying disease biomarkers. Blood serum or plasma is the most commonly used sample for such analysis; however, it presents challenges due to the complexity and dynamic range of protein abundance. Despite these difficulties, the development of high-resolution MS instruments has made comprehensive investigation of blood proteomics possible. The evolution of time-of-flight (TOF) or Orbitrap MS instruments has played a significant role in the field of blood proteomics. These instruments are now among the most prominent techniques for blood proteomics due to their sensitivity, selectivity, fast response, and stability. For optimal results, it is necessary to eliminate high-abundance proteins from the blood sample, to maximize the depth coverage of the blood proteomics analysis. This can be achieved through various methods, including commercial kits, chemically synthesized materials, and MS technologies. This paper reviews recent advancements in MS technology and its remarkable applications in biomarker discovery, particularly in the areas of cancer and COVID-19 studies.

Integrated Microwell Array-Based Microfluidic Chip with a Hand-Held Smartphone-Controlled Device for Nucleic Acid Detection.

In this study, we designed a highly integrated microfluidic chip for nucleic acid extraction, amplification, and detection. Magnetic beads, which are used to capture nucleic acids on the chip, are trapped in the microwell arrays in a one-well-one-bead manner after local surface modification of the inner faces of the microwells. On-chip liquid introduction, delivery, and mixing are all carried out manually with one syringe and no other equipment. A hand-held device with precise temperature control and high-quality imaging is developed, which is only 2.3 cubic decimeters in volume and 1.2 kg in weight. Via the use of the Internet for wireless communication, the experiment and data analysis after inserting the chip into the device can be conducted by a smartphone anywhere there is an Internet connection. We carried out reverse transcription loop-mediated isothermal amplification (RT-LAMP) on the chip with the hand-held device. SARS-CoV-2 pseudoviruses are extracted, reverse transcribed, amplified, and detected on the chip with the hand-held device with satisfactory results. Thus, a highly integrated, easy-to-operate, and rapid nucleic acid detection microfluidic chip with a hand-held smartphone-controlled device is proposed, and this new platform for nucleic acid detection shows great potential for mobile point-of-care testing (POCT).

Precise Control of Trypsin Immobilization by a Programmable DNA Tetrahedron Designed for Ultrafast Proteome Digestion and Accurate Protein Quantification.

In proteomics research, with advantages including short digestion times and reusable applications, immobilized enzyme reactors (IMERs) have been paid increasing attention. However, traditional IMERs ignore the reasonable spatial arrangement of trypsin on the supporting matrixes, resulting in the partial overlapping of the active domain on trypsin and reducing digesting efficiency. In this work, a DNA tetrahedron (DNA TET)-based IMER Fe3O4-GO-AuNPs-DNA TET-Trypsin was designed and prepared. The distance between vertices of DNA TETs effectively controls the distribution of trypsin on the nanomaterials; thus, highly efficient protein digestion and accurate quantitative results can be achieved. Compared to the in-solution digestion (12-16 h), the sequence coverage of bovine serum albumin was up to 91% after a 2-min digestion by the new IMER. In addition, 3328 proteins and 18,488 peptides can be identified from HeLa cell protein extract after a 20-min digestion. For the first time, human growth hormone reference material was rapidly and accurately quantified after a 4-h digestion by IMER. Therefore, this new IMER has great application potential in proteomics research and SI traceable quantification.

Generation, evolution, interfering factors, applications, and challenges of patient-derived xenograft models in immunodeficient mice.

Establishing appropriate preclinical models is essential for cancer research. Evidence suggests that cancer is a highly heterogeneous disease. This follows the growing use of cancer models in cancer research to avoid these differences between xenograft tumor models and patient tumors. In recent years, a patient-derived xenograft (PDX) tumor model has been actively generated and applied, which preserves both cell-cell interactions and the microenvironment of tumors by directly transplanting cancer tissue from tumors into immunodeficient mice. In addition to this, the advent of alternative hosts, such as zebrafish hosts, or in vitro models (organoids and microfluidics), has also facilitated the advancement of cancer research. However, they still have a long way to go before they become reliable models. The development of immunodeficient mice has enabled PDX to become more mature and radiate new vitality. As one of the most reliable and standard preclinical models, the PDX model in immunodeficient mice (PDX-IM) exerts important effects in drug screening, biomarker development, personalized medicine, co-clinical trials, and immunotherapy. Here, we focus on the development procedures and application of PDX-IM in detail, summarize the implications that the evolution of immunodeficient mice has brought to PDX-IM, and cover the key issues in developing PDX-IM in preclinical studies.

Semi-supervised automatic dental age and sex estimation using a hybrid transformer model.

Teeth-based age and sex estimation is an important task in mass disasters, criminal scenes, and archeology. Although various methods have been proposed, most of them are subjective and influenced by observers' experiences. In this study, we aimed to develop a deep learning model for automatic dental age and sex estimation from orthopantomograms (OPGs) and compare to manual methods. A large dataset of 15,195 OPGs (age range, 16 ~ 50 years; mean age, 29.65 years ± 9.36 [SD]; 10,218 females) was used to train and test a hybrid deep learning model which is a combination of convolutional neural network and transformer model. The final performance of this model was evaluated on additional independent 100 OPGs and compared to the manual method for external validation. In the test of 1413 OPGs, the mean absolute error (MAE) of age estimation was 2.61 years by this model. The accuracy and the area under the receiver operating characteristic curve (AUC) of sex estimation were 95.54% and 0.984. The heatmap indicated that the crown and pulp chamber of premolars and molars contain the most age-related information. In the additional independent 100 OPGs, this model achieved an MAE of 3.28 years for males and 3.79 years for females. The accuracy of this model was much higher than that of the manual models. Therefore, this model has the potential to assist radiologists in automated age and sex estimation.

Nanoliter atmospheric pressure photoionization-mass spectrometry for direct bioanalysis of polycyclic aromatic hydrocarbons.

Polycyclic aromatic hydrocarbons (PAHs) are a class of low-polarity environmental contaminants that have severe carcinogenic effects and have drawn worldwide attention. However, there remain challenges for current mass spectrometric ionization techniques in the analysis of low-polarity compounds in small-volume biosamples, such as single cells. In this work, we developed a nanoliter atmospheric pressure photoionization (nano-APPI) source and optimized its parameters for the detection of PAHs in small-volume samples. We evaluated the ionization performance of the source in direct and auxiliary gas-assisted photoionization modes and analyzed different PAH compounds as well as spiked biosamples. By combining the advantages of nano-electrospray ionization (nano-ESI) and atmospheric pressure photoionization (APPI), our newly developed nano-APPI source achieved high sensitivity for the analysis of PAHs down to the fmol level. Compared to conventional atmospheric pressure chemical ionization (APCI), the detection limit of PAHs was increased by 1-2 orders of magnitude. By optimizing various parameters, we achieved highly efficient ionization of PAHs, effective analysis of PAHs in mixed components, and sensitive detection of low-abundance PAHs in single-cell samples. Our optimized nano-APPI source was successfully applied for the sensitive analysis of PAHs in complex biological samples. Based on our study, we believe that nano-APPI holds great promise for toxicological studies on complex biological samples. The present work has implications for improving the detection sensitivity of low-polarity environmental contaminants and advancing the field of MS-based analysis of small-volume biosamples.

A culture-free method for rapidly and accurately quantifying active SARS-CoV-2.

Determining the quantity of active virus is the most important basis to judge the risk of virus infection, which usually relies on the virus median tissue culture infectious dose (TCID50) assay performed in a biosafety level 3 laboratory within 5-7 days. We have developed a culture-free method for rapid and accurate quantification of active severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by targeting subgenomic RNA (sgRNA) based on reverse transcription digital PCR (RT-dPCR). The dynamic range of quantitative assays for sgRNA-N and sgRNA-E by RT-dPCR was investigated, and the result showed that the limits of detection (LoD) and quantification (LoQ) were 2 copies/reaction and 10 copies/reaction, respectively. The delta strain (NMDC60042793) of SARS-CoV-2 was cultured at an average titer of 106.13 TCID50/mL and used to evaluate the developed quantification method. Copy number concentrations of the cultured SARS-CoV-2 sgRNA and genomic RNA (gRNA) gave excellent linearity (R2 = 0.9999) with SARS-CoV-2 titers in the range from 500 to 105 TCID50/mL. Validation of 63 positive clinical samples further proves that the quantification of sgRNA-N by RT-dPCR is more sensitive for active virus quantitative detection. It is notable that we can infer the active virus titer through quantification of SARS-CoV-2 sgRNA based on the linear relationship in a biosafety level 2 laboratory within 3 h. It can be used to timely and effectively identify infectious patients and reduce unnecessary isolation especially when a large number of COVID-19 infected people impose a burden on medical resources.