Biology subjects

Qiu, X.

Publications and source records attributed to Qiu, X..

At least 73 records · Page 4Linked to original sources

bioRxiv2024

Deep Learning Prediction of Glycopeptide Tandem Mass Spectra Powers Glycoproteomics

Protein glycosylation plays a significant role in numerous physiological and pathological cellular functions. Glycoproteomics based on liquid chromatography-tandem mass spectrometry (LC-MS/MS) studies the protein glycosylation on a proteome-wide scale to get combinational information on glycosylation site, glycosylation level and glycan structure. However, the current sequence searching-based methods for glycoproteomics often fall short in glycan structure determination due to the limited occurrence of structure-determining ions. While spectral searching methods can utilize fragment intensity information to facilitate the identification of glycopeptides, its application is hindered by the difficulties in spectral library construction. In this work, we present DeepGP, a hybrid deep learning framework based on Transformer and graph neural network (GNN), for the prediction of MS/MS spectra and retention time of glycopeptides. Two GNN modules are utilized to capture the branched glycan structure and predict glycan ions intensity, respectively. Additionally, a pre-training strategy is implemented to alleviate the insufficiency of glycoproteomics data. Testing on multiple biological datasets, we demonstrate that DeepGP can predict MS/MS spectra and retention time of glycopeptides closely aligning with the experimental results. Comprehensive benchmarking of DeepGP on synthetic and biological datasets validates its effectiveness in distinguishing similar glycoforms. Remarkably, DeepGP can differentiate isomeric glycopeptides using MS/MS spectra without diagnostic ions. Based on various decoy methods, we demonstrated that DeepGP in combination with database searching can significantly increase the detection sensitivity of glycopeptides. We outlook that DeepGP can inspire extensive future work in glycoproteomics.

bioinformatics↗

bioRxiv2024

Mammalian SWI/SNF complex activity regulates POU2F3 and constitutes a targetable dependency in small cell lung cancer

Small cell lung cancers (SCLC) are comprised of heterogeneous subtypes marked by lineage-specific transcription factors, including ASCL1, NEUROD1, and POU2F3. POU2F3-positive SCLC, [~]12% of all cases, are uniquely dependent on POU2F3 itself; as such, approaches to attenuate POU2F3 expression may represent new therapeutic opportunities. Here using genome-scale screens for regulators of POU2F3 expression and SCLC proliferation, we define mSWI/SNF complexes, including non-canonical BAF (ncBAF) complexes, as top dependencies specific to POU2F3-positive SCLC. Notably, clinical-grade pharmacologic mSWI/SNF inhibition attenuates proliferation of all POU2F3-positive SCLCs, while disruption of ncBAF via BRD9 degradation is uniquely effective in pure non-neuroendocrine POU2F3-SCLCs. mSWI/SNF maintains accessibility over gene loci central to POU2F3-mediated gene regulatory networks. Finally, chemical targeting of SMARCA4/2 mSWI/SNF ATPases and BRD9 decrease POU2F3-SCLC tumor growth and increase survival in vivo. Taken together, these results characterize mSWI/SNF-mediated global governance of the POU2F3 oncogenic program and suggest mSWI/SNF inhibition as a therapeutic strategy for SCLC.

cancer biology↗

bioRxiv2024

Increased chromatin accessibility drives transition to androgen receptor splice variant dependence in castration-resistant prostate cancer

Androgen receptor (AR) splice variants, of which ARv7 is the most common, are increased in prostate cancer (PC) that develops resistance to androgen signaling inhibitor drugs, but the extent to which these variants drive AR activity, and whether they have novel functions or dependencies, remain to be determined. We generated a subline of VCaP PC cells (VCaP16) that is resistant to the AR inhibitor enzalutamide (ENZ) and found that AR activity was independent of the full-length AR (ARfl), despite its continued high-level expression, and was instead driven by ARv7. The ARv7 cistrome and transcriptome in VCaP16 cells mirrored that of the ARfl in VCaP cells, although ARv7 chromatin binding was weaker, and strong ARv7 binding sites correlated with higher affinity ARfl binding sites across multiple models and clinical samples. Notably, although ARv7 expression in VCaP cells increased rapidly in response to ENZ, there was a long lag before it gained chromatin binding and transcriptional activity. This lag was associated with an increase in chromatin accessibility, with the AR and nuclear factor I (NFI) motifs being most enriched at these more accessible sites. Moreover, the transcriptional effects of combined NFIB and NFIX knockdown versus ARv7 knockdown were highly correlated. These findings indicate that ARv7 can drive the AR program, but that its activity is dependent on adaptations that increase chromatin accessibility to enhance its intrinsically weak chromatin binding.

cancer biology↗

bioRxiv2024

Cardiac stress leads to regulation of Filamin C dimerisation via an ancient phosphorylation-modulated interaction with HSPB7

The biomechanical properties and responses of tissues underpin a variety of physiological functions and pathologies. In striated muscle, the actin-binding protein filamin C (FLNC) is a key protein whose variants causative for a wide range of cardiomyopathies and musculoskeletal pathologies. Seemingly a multi-functional protein that interacts with a variety of partners, how FLNC is regulated at the molecular level is not well understood. Here we have investigated its interaction with HSPB7, a cardiac-specific molecular chaperone whose absence is embryonically lethal. We found that FLNC and HSPB7 interact in cardiac tissue under biomechanical stress, forming a strong hetero-dimer whose structure we have solved by means of X-ray crystallography. Our quantitative analyses show that the hetero-dimer out-competes the FLNC homo-dimer interface, potentially acting to abrogate the ability of the protein to cross-link the actin cytoskeleton, and to enhance its diffusive mobility. We show that phosphorylation of FLNC at threonine 2677, located at the dimer interface and associated with cardiac stress, acts to favour the homo-dimer. Conversely, phosphorylation at tyrosine 2683, also at the dimer interface, has the opposite effect and shifts the equilibrium towards the hetero-dimer. Evolutionary analysis and ancestral sequence reconstruction reveals this interaction and its mechanisms of regulation to date around the time primitive hearts evolved in chordates. Our work rationalises on the molecular level how FLNC might switch between stabilising functions in the cell, and reveals how HSPB7 acts as a specific molecular chaperone that regulates FLNC.

molecular biology↗

bioRxiv2023

Deciphering Abnormal Platelet Subpopulations in Inflammatory Diseases through Machine Learning and Single-Cell Transcriptomics

IntroductionThe transcriptional heterogeneity of activated platelets, play a significant role in contributing to negative outcomes in sepsis, COVID-19, and autoimmune diseases such as systemic lupus erythematosus (SLE). Despite this, our understanding of these heterogeneous platelet responses remains limited. In this study, we aim to investigate the diverse transcriptional profiles of activated platelets in these diseases, with the goal of deciphering this platelet heterogeneity for new therapeutic strategies to target abnormal and pathogenic platelet subtypes. Materials and methodsWe obtained the single cell transcriptional profiles of blood platelets from patients with COVID-19, sepsis, and SLE. Utilizing machine learning algorithms, Deep Neural Network (DNN) and eXtreme Gradient Boosting (XGB), we discerned the distinct transcriptomic signatures indicative of fatal versus survival clinical outcomes. Our methodological framework incorporated source data annotations and platelet markers and used SingleR and Seurat for detailed profiling. Additionally, we implemented Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction and visualization, aiding in the detection of various platelet subtypes and their correlation with disease status and patient outcomes. ResultsOur study identified distinct platelet subpopulations that are associated with disease severity. We demonstrated that alterations in platelet transcription patterns can exacerbate endotheliopathy, potentially heightening the risk of coagulation in fatal patients. Moreover, these changes can also influence lymphocyte function, indicating a more extensive role for platelets in inflammatory and immune responses. ConclusionsEnhanced transcriptional heterogeneity in activated platelets is linked to adverse outcomes in conditions such as sepsis, COVID-19, and autoimmune diseases. The discovery of these unique platelet subpopulations paves the way for innovative therapeutic strategies targeting platelet activation, which could potentially improve patient outcomes. Summary sentenceSingle-Cell RNA Sequencing Analysis of Platelets from COVID-19, Sepsis, and SLE Reveals Disease Signatures and Treatment Options to Prevent Patient Mortality. Graphical AbstractsO_LIThe platelet to T cell ratio proportion in PBMC was identified as the most potent predictor for distinguishing survivors from fatal patients, underscores the potential of this ratio as a prognostic biomarker. C_LIO_LIThe discovery of different platelet subgroups, especially active coagulation, hypoxic, and quiescent clusters, in fatal COVID-19 patients, indicates potential targeted treatment strategies. C_LIO_LIIn patients with severe and fatal conditions, we observed three key phenomena: the aggregation of platelets with monocytes, the amplification of endothelial dysfunction by platelets, and a decrease in lymphocyte activation and differentiation due to platelets. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=197 SRC="FIGDIR/small/572680v1_ufig1.gif" ALT="Figure 1"> View larger version (64K): org.highwire.dtl.DTLVardef@141ca55org.highwire.dtl.DTLVardef@4ad1aborg.highwire.dtl.DTLVardef@b400forg.highwire.dtl.DTLVardef@9ad44e_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

Utilizing Non-Invasive Prenatal Test Sequencing Data Resource for Human Genetic Investigation

Non-invasive prenatal testing (NIPT) employs ultra-low-pass sequencing of maternal plasma cell-free DNA to detect fetal trisomy. With exceptional sensitivity, specificity, and safety, NIPT has gained global adoption, exceeding ten million tests, establishing it as one of the largest human genetic resources. This resource holds immense potential for exploring population genetic variations and their correlations with phenotypes. Here, we present comprehensive methods tailored for analyzing large, low-depth NIPT genetic datasets, involving customized algorithms and software for genetic variation detection, genotype imputation, and genome-wide association analysis. Through evaluations, we demonstrate that, when integrated with appropriate probabilistic models and population-specific haplotype reference panels, accurate allele frequency estimation and high genotype imputation accuracy (0.8 to 0.9) are achievable for genetic variants with alternative allele frequencies between 0.01 and 0.05, at sequencing depths of 0.1x to 0.25x. Additionally, we attained an R-square exceeding 0.9 for estimating genetic effect sizes across various sequencing platforms. These findings establish a robust theoretical and practical foundation for leveraging NIPT data in advancing medical genetic studies, not only in realms of maternal and child health, but also for long-term health outcomes. HighlightsO_LIIntroduction of probabilistic model integration for analyzing large-scale, low-pass non-invasive prenatal test (NIPT) sequencing data C_LIO_LIEvaluation of protocols for variant detection, genotype imputation, and genome-wide association analyses with NIPT data C_LI

bioinformatics↗

bioRxiv2023

Structural variation discovery in wheat using PacBio high-fidelity sequencing

BackgroundStructural variations (SVs) pervade plant genomes and contribute substantially to the phenotypic diversity. However, most SVs were ineffectively assayed because of their complex nature and the limitations of early genomic technologies. The recent advance in third-generation sequencing, particularly the PacBio high-fidelity (HiFi) sequencing technology, produces highly accurate long-reads and offers an unprecedented opportunity to characterize SVs structure and functionality. As HiFi sequencing is relatively new to population genomics, it is imperative to evaluate and optimize HiFi sequencing based SV detection before applying the technology at scale. ResultsWe sequenced wheat genomes using HiFi reads, followed by a comprehensive evaluation of mainstream long-read aligners and SV callers in SV detection. The results showed that the accuracy of deletion discovery is markedly influenced by callers, which account for 87.73% of the variance, while both aligners (38.25%) and callers (49.32%) contributed substantially to the accuracy variance for insertions. Among the aligners, Winnowmap2 and NGMLR excelled in detecting deletions and insertions, respectively. For SV callers, SVIM achieved the best performance. We demonstrated that combining the aligners and callers mentioned above is optimal for SV detection. Furthermore, we evaluated the effect of sequencing depth on the accuracy of SV detection, showing that low-coverage HiFi sequencing is sufficiently robust for high-quality SV discovery. ConclusionsThis study thoroughly evaluated SV discovery approaches using HiFi reads, establishing optimal workflows to investigate structural variations in the wheat genome. The notable accuracy of SV discovery from low-coverage HiFi sequencing indicates that skim HiFi sequencing is effective and preferable to characterize SVs at the population level. This study will help advance SV discovery and decipher the biological functions of SVs in wheat and many other plants.

genomics↗

bioRxiv2023

E-proteins set the threshold for optimal TCF1 expression during αβ T cell development.

Expression of T Cell Factor-1 (TCF1), encoded by Tcf7, regulates lineage fate decisions during T cell development. Here we demonstrate that E-proteins control the threshold of TCF1 expression required for development of T cells. E-proteins bind to five elements (EPEs) in the Tcf7 locus. The third element, EPE3, interacts directly with Tcf7 promoter in Hi-ChIP analyses, suggesting it is an active enhancer. CRISPR-ablation of EPE3 reduces TCF1 protein expression in precursor thymocytes by 2-fold and dramatically impairs development of {beta} and {gamma}{delta} T cells. Single cell gene expression analysis identified differentiation blocks at multiple CD4-CD8- stages and subsequent transition to CD4+CD8+ stage. These data identify E-proteins and EPE3 as critical for the optimal TCF1 expression required for T cell development.

immunology↗

bioRxiv2023

BioKG: a comprehensive, high-quality biomedical knowledge graph for AI-powered, data-driven biomedical research

To address the rapid growth of scientific publications and data in biomedical research, knowledge graphs (KGs) have become a critical tool for integrating large volumes of heterogeneous data to enable efficient information retrieval and automated knowledge discovery (AKD). However, transforming unstructured scientific literature into KGs remains a significant challenge, with previous methods unable to achieve human-level accuracy. In this study, we utilized an information extraction pipeline that won first place in the LitCoin NLP Challenge (2022) to construct a large-scale KG named iKraph using all PubMed abstracts. The extracted information matches human expert annotations and significantly exceeds the content of manually curated public databases. To enhance the KGs comprehensiveness, we integrated relation data from 40 public databases and relation information inferred from high-throughput genomics data. This KG facilitates rigorous performance evaluation of AKD, which was infeasible in previous studies. We designed an interpretable, probabilistic-based inference method to identify indirect causal relations and applied it to real-time COVID-19 drug repurposing from March 2020 to May 2023. Our method identified 600-1400 candidate drugs per month, with one-third of those discovered in the first two months later supported by clinical trials or PubMed publications. These outcomes are very challenging to attain through alternative approaches that lack a thorough understanding of the existing literature. A cloud-based platform (https://biokde.insilicom.com) was developed for academic users to access this rich structured data and associated tools.

bioinformatics↗

bioRxiv2023

Graph-Dynamo: Learning stochastic cellular state transition dynamics from single cell data

Modeling cellular processes in the framework of dynamical systems theories is a focused area in systems and mathematical biology, but a bottleneck to extend the efforts to genome-wide modeling is lack of quantitative data to constrain model parameters. With advances of single cell techniques, learning dynamical information from high throughput snapshot single cell data emerges as an exciting direction in single cell studies. Our previously developed dynamo framework reconstructs generally nonlinear genome-wide gene regulation relations from single cell expression state and either splicing- or metabolic labeling-based RNA velocity data. In this work, we first developed a graph-based machine learning procedure that imposes a mathematical constraint that the RNA velocity vectors lie in the tangent space of the low-dimensional manifold formed by the single cell expression data. Unlike a popular cosine correlation kernel used in literature, this tangent space projection (TSP) preserves the magnitude information of a vector when one transforms between different representations of the data manifold. Next, we formulated a data-driven graph Fokker-Planck (FPE) equation formalism that models the full cellular state transition dynamics as a convection-diffusion process on a data-formed graph network. The formalism is invariant under representation transformation and preserves the topological and dynamical properties of the system dynamics. Numerical tests on synthetic data and experimental scRNA-seq data demonstrate that the graph TSP/FPE formalism built from snapshot single cell data can recapitulate system dynamics. Significance StatementA cell is a dynamical system, which responds to extracellular and intracellular cues and changes its internal state. In practice the internal state of a cell is often characterized by its gene expression profile such as its transcriptome measured through destructive single cell techniques. Just like one can use Newtons equations to describe motions of the celestial bodies in the solar system, the state change of a cell in principle can also be described by a set of mathematical equations. Determining the form and associated parameters of such equations, however, is challenging. This work presented a general framework of reconstructing dynamical equations from snapshot single cell data.

biophysics↗

bioRxiv2023

Bayesian phylogenetics on globally emerging SARS-CoV-2 variant BA.2.86 suggest global distribution and rapid evolution

Using bioinformatic pipelines and Bayseian phylogenetic analyses, we characterized a SARS-CoV-2 variant designated by the World Health Organization as a variant under monitoring in August 2023. Here we analyze the genomes of this SARS-CoV-2 variant, BA.2.86, deposited into GISAID within the two weeks of its emergence (2023-08-14 first submission to 2023-08-31), including the first BA.2.86 genome reported from a traveler originating from Japan. We present bioinformatics methods using publicly available tools to help analysts identify the lineage-defining 12 nucleotide insertion (S:Ins16MPLF), which is often masked by most bioinformatics pipelines. We also applied maximum-likelihood and Bayesian phylogenetics to demonstrate the high mutational rate of the tree branch leading to the emergence of BA.2.86, hinting at possible origins, and predict that BA.2.86 emerged around May 2023 and spread globally rapidly. Taken together, these results provide a framework for more rigorous bioinformatics approaches for teams performing genomic surveillance on viral respiratory pathogens.

bioinformatics↗

bioRxiv2023

Mitochondrial proteostasis mediated by CRL5Ozz and Alix maintains skeletal muscle function

High energy-demanding tissues, such as skeletal muscle, require mitochondrial proteostasis to function properly. Two quality-control mechanisms, the ubiquitin proteasome system (UPS) and the release of mitochondria-derived vesicles, safeguard mitochondrial proteostasis. However, whether these processes interact is unknown. Here we show that the E3 ligase CRL5Ozz, a member of the UPS, and its substrate Alix control the mitochondrial concentration of Slc25A4, a solute carrier that is essential for ATP production. The mitochondria in Ozz-/- or Alix-/-skeletal muscle share overt morphologic alterations (they are supernumerary, swollen, and dysmorphic) and have abnormal metabolomic profiles. We found that CRL5Ozz ubiquitinates Slc25A4 and promotes its proteasomal degradation, while Alix facilitates SLC25A4 loading into exosomes destined for lysosomal destruction. The loss of Ozz or Alix offsets steady-state levels of Slc25A4, which disturbs mitochondrial metabolism and alters muscle fiber composition. These findings reveal hitherto unknown regulatory functions of Ozz and Alix in mitochondrial proteostasis.

cell biology↗

bioRxiv2023

Improving the Circularization Efficiency, Stability and Translatability of Circular RNA by circDesign

Synthetic circular RNA (circRNA) has emerged as a promising platform for vaccine and therapeutic development, featuring its uniqueness in a closed-loop structure, cap-independent translation mechanism, and prolonged expression. However, the rational design of a circRNA sequence to jointly improve its stability and protein coding potential remains challenging. In this study, we present circDesign, an efficient algorithm to achieve the optimal design of circRNA by ensuring optimized folding of each segment, which leads to enhanced circularization efficiency, stability, and translatability. Using rabies virus glycoprotein (RABV-G) as the model antigen, we demonstrated that circDesign-generated circRNAs exhibited higher stability and protein translation efficiency in vitro and in vivo compared to other codon adaptation index (CAI)-optimized sequences, thus leading to enhanced in vivo immunogenicity. Ribosome and polysome profiling further revealed that an intact internal ribosome entry site (IRES) structure is critical for efficient translation. By intentionally disrupting the IRES motifs, we observed that the resulting circRNA sequences had lower translatability compared to circDesign-generated sequences. Taken together, our circular RNA design algorithm provides a general strategy to leverage the capability of circRNA as next-generation vaccines or therapeutics.

bioengineering↗

bioRxiv2023

Sequence-dependent orientational coupling and electrostatic attraction in cation-mediated DNA-DNA interactions

Condensation of DNA is vital for its biological functions and controlled nucleic acid assemblies. However, the mechanisms of DNA condensation are not fully understood due to the inability of experiments to access cation distributions and the complex interplay of energetic and entropic forces during assembly. By constructing free energy surfaces using exhaustive sampling, and detailed analysis of cation distributions, we elucidate the mechanism of DNA condensation in different salt conditions and with different DNA sequences. We found that DNA condensation is facilitated by the correlated dynamics of localized cations at the grooves of DNA helices. These dynamics are strongly dependent on salt conditions and DNA sequences. In the presence of magnesium ions, major groove binding facilitates attraction. In contrast, in the presence of poly-valent cations, minor groove binding serves to create charge patterns leading to condensation. Our findings present a novel advancement to the field and have broad implications for understanding and controlling nucleic acid complexes in vivo and in vitro.

biophysics↗

bioRxiv2023

Storm: Incorporating transient dynamics to infer the RNA velocity with metabolic labeling information

AO_SCPLOWBSTRACTC_SCPLOWThe time-resolved scRNA-seq (tscRNA-seq) provides the possibility to infer physically meaningful kinetic parameters, e.g., the transcription, splicing or RNA degradation rate constants with correct magnitudes, and RNA velocities by incorporating temporal information. Previous approaches utilizing the deterministic dynamics and steady-state assumption on gene expression states are insufficient to achieve favorable results for the data involving transient process. We present a dynamical approach, Storm (Stochastic models of RNA metabolic-labeling), to overcome these limitations by solving stochastic differential equations of gene expression dynamics. The derivation reveals that the new mRNA sequencing data obeys different types of cell-specific Poisson distributions when jointly considering both biological and cell-specific technical noise. Storm deals with measured counts data directly and extends the RNA velocity methodology based on metabolic labeling scRNA-seq data to transient stochastic systems. Furthermore, we relax the constant parameter assumption over genes/cells to obtain gene-cell-specific transcription/splicing rates and gene-specific degradation rates, thus revealing time-dependent and cell-state specific transcriptional regulations. Storm will facilitate the study of the statistical properties of tscRNA-seq data, eventually advancing our understanding of the dynamic transcription regulation during development and disease.

systems biology↗

bioRxiv2023

scATAnno: Automated Cell Type Annotation for single-cell ATAC-seq Data

Recent advances in single-cell epigenomic techniques have created a growing demand for scATAC-seq analysis. One key analysis task is to determine cell type identity based on the epigenetic data. We introduce scATAnno, a python package designed to automatically annotate scATAC-seq data using large-scale scATAC-seq reference atlases. This workflow generates the reference atlases from publicly available datasets enabling accurate cell type annotation by integrating query data with reference atlases, without the use of scRNA-seq data. To enhance annotation accuracy, we have incorporated KNN-based and weighted distance-based uncertainty scores to effectively detect cell populations within the query data that are distinct from all cell types in the reference data. We compare and benchmark scATAnno against 7 other published approaches for cell annotation and show superior performance in multiple data sets and metrics. We showcase the utility of scATAnno across multiple datasets, including peripheral blood mononuclear cell (PBMC), Triple Negative Breast Cancer (TNBC), and basal cell carcinoma (BCC), and demonstrate that scATAnno accurately annotates cell types across conditions. Overall, scATAnno is a useful tool for scATAC-seq reference building and cell type annotation in scATAC-seq data and can aid in the interpretation of new scATAC-seq datasets in complex biological systems.

bioinformatics↗

bioRxiv2023

De Novo Design of κ-Opioid Receptor Antagonists Using a Generative Deep Learning Framework

Likely effective pharmacological interventions for the treatment of opioid addiction include attempts to attenuate brain reward deficits during periods of abstinence. Pharmacological blockade of the {kappa}-opioid receptor (KOR) has been shown to abolish brain reward deficits in rodents during withdrawal, as well as to reduce the escalation of opioid use in rats with extended access to opioids. Although KOR antagonists represent promising candidates for the treatment of opioid addiction, very few potent selective KOR antagonists are known to date and most of them exhibit significant safety concerns. Here, we used a generative deep learning framework for the de novo design of chemotypes with putative KOR antagonistic activity. Molecules generated by models trained with this framework were prioritized for chemical synthesis based on their predicted optimal interactions with the receptor. Our models and proposed training protocol were experimentally validated by binding and functional assays.

bioinformatics↗

bioRxiv2023

NULISA: a novel proteomic liquid biopsy platform with attomolar sensitivity and high multiplexing

The blood proteome holds great promise for precision medicine but poses substantial challenges due to the low abundance of most plasma proteins and the vast dynamic range across the proteome. We report a novel proteomic technology - NUcleic acid Linked Immuno-Sandwich Assay (NULISA) - that incorporates a dual capture and release mechanism to suppress the assay background and improves the sensitivity of the proximity ligation assay by over 10,000-fold to the attomolar level. It utilizes pairs of antibodies conjugated to DNA oligonucleotides that enable immunocomplex purification and generate reporter DNA containing target- and sample-specific barcodes for a next-generation sequencing-based, highly multiplexed readout. A 200-plex NULISA targeting 124 cytokines and chemokines and 80 other immune response-related proteins demonstrated superior sensitivity for detecting low-abundance proteins and high concordance with other immunoassays. The ultrahigh sensitivity allowed the detection of previously difficult-to-detect, but biologically important, low-abundance biomarkers in patients with autoimmune diseases and COVID-19. Fully automated NULISA addresses longstanding challenges in proteomic analysis of liquid biopsies and makes broad and in-depth proteomic analysis accessible to the general research community and future diagnostic applications.

genomics↗