Biology subjects

Deng, J.

Publications and source records attributed to Deng, J..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Multi-component functionalized Bifidobacterium longum hydrogel for multi-target integrated therapy of colitis-associated anxiety and depression

Inflammatory bowel diseases (IBDs) are frequently accompanied by anxiety and depression, largely driven by perturbed gut-brain axis signaling. However, current oral therapies remain constrained by the spatial and functional separation between intestinal inflammation and central nervous system dysfunction. Here, we present a comprehensive gut-brain dual region integrated therapeutic strategy based on functionalized Bifidobacterium longum hydrogel (INPs@BL@Gel), in which baicalin and tyrosine are coordinated with Fe(III) to form infinite coordination polymers (ICPs), coated with inulin, assembled onto Bifidobacterium longum (BL), and subsequently encapsulated within a pH- and matrix metalloproteinase-responsive silk fibroin-gelatin hydrogel. INPs@BL@Gel exhibits high drug-loading, effective gastric protection, inflammation-triggered release, and long-term intestinal colonization. Within the inflamed intestine, BL and components synergistically suppress inflammatory responses, restore gut microbiota homeostasis, and promote intestinal barrier repair through multi-target integrated therapy. Importantly, BL combined with components markedly enhances the production of beneficial neuroactive metabolites such as homovanillic acid and short-chain fatty acids, which integrated regulate neuroinflammation, preserve synaptic function, and facilitate blood-brain barrier repair via the gut-brain axis. In vivo studies demonstrate that INPs@BL@Gel not only exert potent therapeutic efficacy against colitis and effectively alleviate associated depression, but also reshape the gut microbiota and restore barrier integrity, achieving an remarkable comprehensive therapeutic effect. O_FIG O_LINKSMALLFIG WIDTH=158 HEIGHT=200 SRC="FIGDIR/small/710940v1_fig1a.gif" ALT="Figure 11"> View larger version (59K): org.highwire.dtl.DTLVardef@1a831f3org.highwire.dtl.DTLVardef@1676617org.highwire.dtl.DTLVardef@15f99c1org.highwire.dtl.DTLVardef@1677cc4_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOScheme 1.C_FLOATNO (a) Schematic diagram of the design and preparation of functionalized Bifidobacterium longum hydrogel. (b) Exploration of the mechanism of INPs@BL@Gel in treating colitis-associated anxiety and depression through a dual-site multi-target synergistic strategy. C_FIG

pathology↗

bioRxiv2026

Shape Factor Analysis as a Quantitative Framework for Assessing Spheroid and Organoid Morphology and Invasiveness

Morphological changes of spheroids and organoids are widely used as in vitro indicators of healthy and diseased tissue functions; however, quantitative methods to classify spheroid and organoid morphology are limited. In clinical breast imaging, radiologists use tumor shape as a prognostic marker, with irregular margins associated with invasive disease and increased malignancy. Here, we adapted this approach for translational research and developed a custom MATLAB algorithm to quantify the variance in radial lengths of invasive protrusions in spheroids and organoids. First, we analyzed digital phantoms by both ImageJ/FIJI shape descriptors and our radial length analysis to evaluate the capabilities of each measurement method. Subsequently, we performed the same comparisons with images from experimental spheroid and organoid datasets. We demonstrate that multivariate shape factor analysis, including radial length analysis, enables more reliable and comprehensive quantification of spheroid and organoid morphologies than standard shape descriptors alone. By enabling numerical morphological readouts, shape factor analysis can enhance phenotypic profiling of spheroids and organoids and provide valuable metrics for in vitro studies including high-throughput and drug screening workflows.

bioengineering↗

bioRxiv2026

A Novel Glycoproteomics Platform for High-Throughput Identification of Disease-Associated Glycoforms

Glycosylation is a critical post-translational modification, and its aberrant forms are potent disease biomarkers; however, the comprehensive, site-specific identification of all glycosites and glycoforms across an entire proteome remains prohibitively slow and computationally demanding. To address this bottleneck and accelerate biomarker discovery, we introduce the Glycoproteomics Data Analysis Software (GDAS), a novel, high-throughput platform designed to provide confident, proteome-scale identification of disease-specific glycoforms. GDAS streamlines the analysis through a core, multi-step workflow: it initially employs an ultrafast open search (e.g., MSFragger-Glyco) on mass spectrometry data to rapidly screen and statistically reduce the vast proteome database to a manageable subset of significantly regulated glycoproteins, conserving computational resources for subsequent, in-depth, targeted N- and O-glycosylation analysis using specialized tools (e.g., GlycReSoft and O-Pair). Furthermore, a unique Final Analysis Module utilizes an advanced statistical and machine learning pipeline (incorporating Bootstrap/Bayesian methods, XGBoost, and Random Forest) to integrate quantitative results and generate a robust, comprehensive glycosylation score. We demonstrate GDASs power to recognize biologically relevant glycosylation changes in targeted proteins by validating it using published Alzheimers disease data. GDAS can be downloaded from https://github.com/yang-lab/GDAS. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=92 SRC="FIGDIR/small/708406v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@189b270org.highwire.dtl.DTLVardef@1221045org.highwire.dtl.DTLVardef@15a3f50org.highwire.dtl.DTLVardef@1f2d43f_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2026

Circular RNA identification using a genomic language model and a small number of authenticated examples

Genomic language models (gLMs) hold great promise for deciphering biological sequences, yet their effectiveness is hindered by the limited number of experimentally verified examples available for model training, a ubiquitous bottleneck for supervised machine learning. To overcome this challenge, we developed circFormer, the first gLM-driven approach for circular RNA (circRNA) identification. circFormer integrates curriculum learning with gLM fine-tuning: a Nucleotide Transformer model is first trained on a small set of validated circRNAs, the resulting model is used as a teacher to score [~]2.3 million noisy candidates, and the model is then fine-tuned with the noisy candidates along with their scores to improve prediction. Operating either as a standalone predictor or as a filter for existing pipelines, circFormer consistently outperformed traditional machine-learning approaches in accuracy and robustness. Among 50 circFormer-selected candidates that were overlooked by most existing tools, experimental validation using RNase R digestion and RT-qPCR confirmed 94.1% (32/34) of the evaluable candidates as genuine circRNAs. To enhance interpretability, we introduced a model-agnostic, dual-level explainable AI strategy that reveals mechanistic signatures of circRNA formation. circFormer provides a scalable, interpretable, and generalizable framework for converting noisy high-throughput data into reliable functional annotations, highlighting a practical path forward for gLM-based genomics in data-scarce settings.

bioinformatics↗

bioRxiv2026

NRF2 pathway activation and SPP1⁺TREM2⁺ macrophages drive chemoradiotherapy resistance in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is among the most aggressive cancers, with low rates of durable response to chemoradiotherapy and limited therapeutic options for relapsed disease. To uncover mechanisms of treatment resistance and relapse, we performed comprehensive multi-omics profiling of >100 pre-treatment and post-relapse ESCC tumors from a prospective clinical trial (NCT04694391), integrating whole-exome/genome sequencing, bulk RNA-sequencing, single-cell RNA sequencing, and spatial transcriptomics. We identify somatic alterations in NFE2L2/KEAP1 in nearly 40% of relapsed patients, which are associated with upregulation of NRF2 signaling targets in resistant tumors and cell line models. At single-cell and spatial resolution, relapsed tumors are enriched for NRF2-activated epithelial cells that physically co-localize with immunosuppressive SPP1TREM2 macrophages. This co-localization suggests a synergistic interaction between NRF2-driven tumor programs and macrophage-mediated immune suppression that promotes relapse after chemoradiotherapy. Our findings nominate NFE2L2/KEAP1 mutations as predictive biomarkers for patient stratification and highlight therapeutic targeting of NRF2 signaling and SPP1TREM2 macrophages as rational strategies to overcome resistance in ESCC.

cancer biology↗

bioRxiv2026

Integrative multi-omics and multi-trait analysis prioritizes regulatory mechanisms and genes for metabolic dysfunction-associated steatotic liver disease

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a prevalent condition that progresses from simple steatosis to advanced fibrosis, significantly affecting liver function and systemic health. Despite its widespread impact, therapeutic options are limited, highlighting the urgent need for comprehensive exploration to identify potential therapeutic targets. In this study, we created an analysis pipeline anchored on liver gene expression, integrating differential meta-analysis of transcriptomic data across three MASLD stages, transcriptome-wide Mendelian randomization (MR), and transcriptome-wide association studies (TWAS), to identify 39 candidate genes potentially involved in MASLD progression. Furthermore, we prioritized these genes using a scoring system that incorporated gene expression-clinical phenotype correlation meta-analysis, proteome-wide association studies (PWAS), and external genetic data from the GWAS Catalog and ExPheWAS. Single-nucleus RNA sequencing (snRNA-seq) analysis of liver cells from healthy to cirrhotic stages revealed stage- and cell-type-specific expression patterns of these prioritized genes. Through experimental validation in a lipid overload hepatocyte model, we confirmed the role of MLIP in lipid metabolism. These findings, available through an interactive web portal (masldportal.net), provide valuable insights into MASLD mechanisms and offer an easy-accessible resource for the research community. Graphic abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/706502v2_ufig1.gif" ALT="Figure 1"> View larger version (58K): org.highwire.dtl.DTLVardef@184c03eorg.highwire.dtl.DTLVardef@6965ccorg.highwire.dtl.DTLVardef@19a5937org.highwire.dtl.DTLVardef@576b53_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2026

A Single-Cell Temporal Atlas of Mouse Nasal Embryonic Development

The mammals breathe and perceive odors through anatomically and functionally distinct respiratory and olfactory regions of the nose; however, how the early embryo endows this system remains largely unknown. Here, we present a temporal high-resolution single-cell RNA sequencing (scRNA-seq) atlas of mouse nasal development, comprising 183,247 cells collected from 20 embryos spanning embryonic day 10.5 (E10.5) to E18.5. We identified 7 major cell clusters and 52 molecularly defined subtypes, including previously unrecognized rare populations and niche-derived signaling components involved in progenitor cell maintenance. Lineage reconstruction of epithelial and mesenchymal compartments revealed dynamic cell fate transitions and highlighted candidate regulators of lineage specification, including Foxa1 as a regulator of nasal respiratory ciliated epithelium differentiation. Integration with adult nasal epithelium datasets further delineated transcriptional dynamics of olfactory stem cells. This study provides a molecular roadmap of nasal development and offers a valuable resource for future research into nasal biology and regenerative strategies.

developmental biology↗

bioRxiv2026

A novel uN2CpolyG Transgenic Mouse Model Recapitulates Multisystemic polyG Proteinopathy Pathology of Neuronal Intranuclear Inclusion Disease

Neuronal intranuclear inclusion disease (NIID) is a polyglycine disease that primarily affects the neuronal and neuromuscular systems. Here, we developed a novel transgenic mouse model that faithfully recapitulates the multisystemic impairments associated with polyG intranuclear inclusions. Our findings demonstrate that polyG expression induces neurodegeneration, behavioral deficits, and age-dependent accumulation of uN2CpolyG aggregates across multiple tissues.

animal behavior and cognition↗

bioRxiv2026

Plant-specific and conserved mechanisms of the polymerase-associated factor 1 complex in replication stress responses

DNA replication stress threatens genome stability in eukaryotes. The evolutionarily conserved kinase WEE1 is essential for the activation of the replication stress response. Although the polymerase-associated factor 1 complex (PAF1C) is highly conserved in eukaryotes, its role in the DNA replication stress response remains unclear. Here, we show that Arabidopsis PAF1C is essential for replication stress response. PAF1C-deficient mutants exhibit hypersensitivity to hydroxyurea (HU)-induced replication stress. Mechanistically, we uncover a plant-specific regulatory pathway in which WEE1 interacts with and phosphorylates the PAF1 subunit within its unique N-terminal domain, thereby preventing PAF1 polyubiquitination and subsequent proteasomal degradation to ensure PAF1 accumulation under stress. Genetically, overexpression of a phospho-mimetic PAF1 variant suppresses the HU hypersensitivity of wee1, revealing that PAF1 acts downstream of WEE1. However, the WEE1-PAF1 regulatory axis is absent in yeast, indicating its lineage-specific innovation. Further studies reveal that the replication factor C (RFC) complex interacts with and recruits PAF1 to the stalled replication forks. PAF1 then sequentially recruits the E2 ubiquitin-conjugating enzymes UBC1/2 and the E3 ubiquitin ligases HUB1/2 to promote histone H2B monoubiquitination (H2Bub), thereby facilitating replication fork stability. This RFC-dependent recruitment mechanism is conserved in yeast. Collectively, this study suggests that PAF1 regulates replication stress responses by integrating a plant-specific protein stability control mechanism (WEE1-PAF1) with a conserved recruitment mechanism (RFC-PAF1-UBC1/2-HUB1/2), uncovering a novel function of PAF1C and revealing new mechanisms of WEE1 and the RFC complex.

plant biology↗

bioRxiv2026

Multimodal Behavior Scoring quantifies depression-like severity across chronic stress models and identifies stress-resilient mice

IntroductionRodent chronic-stress models are central to preclinical depression research, yet behavioral phenotypes are often assessed assay-by-assay, which can reduce cross-cohort comparability and mask heterogeneity across symptom domains (e.g., anhedonia, anxiety-like behavior, and behavioral despair). An integrative behavioral index may provide a more robust and interpretable measure of overall stress-induced severity and facilitate stratification of susceptible versus resilient individuals. MethodsWe developed the Multimodal Behavior Scoring (MBS) algorithm to integrate outcomes from standard assays--sucrose preference test (SPT), open field test (OFT), and forced swim test (FST)--into a unified severity score. MBS was evaluated in two independent paradigms, chronic unpredictable mild stress (CUMS) and chronic social defeat stress (CSDS), quantifying group discrimination, reproducibility (intraclass correlation coefficient, ICC), and phenotypic stratification. ResultsMBS robustly separated stressed from control mice (CUMS: +39.8%, p<0.0001; CSDS: +121.8%, p<0.0001) and showed high cross-cohort reproducibility (ICC>0.88). MBS further identified stress-resilient subpopulations and uncovered paradigm-specific behavioral signatures, with despair-dominant patterns in CUMS (FST-MBS, r=0.61) and anxiety-centric patterns in CSDS (OFT-MBS, r=-0.74). Excluding TST did not reduce discrimination performance. ConclusionMBS provides an integrative framework for quantifying depression-like behavioral severity and heterogeneity, enabling streamlined protocols and improving the translational utility of preclinical cohorts for biomarker discovery and antidepressant screening.

animal behavior and cognition↗

bioRxiv2026

Interferon Beta Drives Therapy Resistance in a Patient Derived Model of High Grade Serous Ovarian Cancer

Cancer cell-autonomous type 1 interferon (IFN-1) production and signaling is frequently activated in response to DNA damage and has been associated with the development of therapy resistance in several cancer types. However, its cell-autonomous role in driving resistance in high-grade serous ovarian cancer (HGSOC), a disease defined by near-universal exposure to genotoxic therapy as frontline treatment, remains unclear. Specifically, whether IFN-1 functions in HGSOC as only a response to genotoxic stress or can independently act in driving resistance phenotypes has not been studied. Utilizing a syngeneic patient-derived model of cisplatin-sensitive (SE) and -resistant (CR) HGSOC, we demonstrate that chronic cisplatin exposure is associated with enrichment of IFN-1 signaling and the interferon-related DNA damage resistance signature (IRDS). Acute cisplatin treatment elicited dynamic, temporal IFN-1 signaling and responses in both sensitive and resistant cells, indicating a conserved stress response in resistant cells. Chronic, low-level exposure to exogenous IFN{beta}, in the absence of a DNA-damaging agent, was sufficient to phenocopy several features of chronic cisplatin driven resistance, including reduced therapeutic sensitivity, cell cycle arrest, and decreased proliferation. Notably, IFN{beta} driven resistance occurred without sustained IRDS or canonical interferon stimulated gene (ISG) induction, revealing alternative mechanisms for IFN-1 mediated therapy resistance. Together, these findings identify IFN{beta} as a functional driver of the development of resistance-associated phenotypes and highlight cell-autonomous IFN-1 signaling as a potential biomarker for resistance and a therapeutic target in platinum-resistant disease.

cancer biology↗

bioRxiv2026

Poly(A)-tail-length-dependent surveillance of viral RNA by PABPC1 orchestrates broad-spectrum antiviral defense

RNA processing and modification are critical for virus replication and pathogenesis, yet how the host exploits viral RNA features--particularly the poly(A) tail--for antiviral defense remains unclear. Through multi-omics integration and systematic functional screening, we identify the poly(A)-binding protein PABPC1 as a broad-spectrum restriction factor against multiple coronaviruses. We demonstrate that PABPC1 preferentially binds viral RNAs bearing short poly(A) tails--distinct from the longer and more heterogeneous host poly(A) tails--and, in a poly(A)-length-dependent manner, recruits the mitochondrial exonuclease EXD2 to assemble a degradative RNA-protein complex. Both the recognition and subsequent degradation of viral RNA strictly depend on poly(A) tail length, enabling selective decay of short-tailed viral transcripts while sparing host mRNAs. Importantly, inflammatory signaling enhances the expression of PABPC1 and EXD2, suggesting its role as an inducible host defense pathway activated during viral infection. Capitalizing on this discovery, we engineered a synthetic fusion protein mimicking the PABPC1-EXD2 complex and achieved efficient delivery using lipid nanoparticles (LNPs). This rationally designed therapeutic exhibits robust suppression of coronavirus replication in both cellular and murine models. Collectively, our findings uncover a novel host antiviral strategy that targets a conserved viral RNA structural element and provide a conceptual framework for developing host-derived and broad-spectrum anti-coronavirus therapeutics.

microbiology↗

bioRxiv2025

M1C is a druggable target for NSCLC KRAS G12C mutant tumors resistant to KRAS inhibitors

Treatment of NSCLC KRAS G12C mutant tumors with the allele-selective sotorasib inhibitor is invariably associated with acquired resistance. The MUC1-encoded oncogenic M1C protein is necessary for self-renewal of NSCLC KRAS mutant cells. We report that treatment of NSCLC KRAS G12C cells with sotorasib induces M1C expression by a STAT1-dependent pathway. In turn, M1C drives sotorasib resistance by NF-{kappa}B-mediated induction of the epithelial-mesenchymal transition (EMT). Targeting M1C(R)NF-{kappa}B signaling (i) suppresses EMT, and (ii) reverses sotorasib resistance. Of translational relevance, treatment with a M1C antibody-drug conjugate (ADC) is effective against sotorasib-resistant NSCLC KRAS G12C cell line and patient-derived tumor xenografts. Clinically, targeted treatment of patients with NSCLC KRAS G12C tumors overexpressing MUC1 associates with decreases in overall survival. These findings identify M1C as a key effector of sotorasib resistance and as a target for treatment of patients with refractory NSCLC KRAS G12C mutant tumors.

cancer biology↗

bioRxiv2025

MAAD: Multidimensional Antiviral Antibody Database

Antibodies have emerged as central components of therapeutic strategies against viral infectious diseases, functioning as key effectors in both prevention and treatment. While traditional antibody discovery has relied heavily on high-throughput screening, the field is now shifting toward rational antibody design, which requires integrative insights into sequence-structure-function relationships. However, the absence of a standardized and well-annotated antibody database integrating these multidimensional features hampers systematic exploration, cross-pathogen comparison, and rational antibody design. Here, we introduce a "Multidimensional Antiviral Antibody Database" (MAAD; http://www.xxx), a curated platform dedicated to antibody nanobody and single-chain variable fragment targeting three high-impact RNA virus families, Coronaviridae (SARS-CoV-1, SARS-CoV-2, MERS-CoV), Orthomyxoviridae (influenza virus), and Pneumoviridae (respiratory syncytial virus, human metapneumovirus), due to the large, high-quality datasets accumulated in recent years. MAAD further incorporates a suite of interactive analysis modules, including CDR annotation, similarity-based CDR3 sequence analysis, V/J gene usage profiling, sequence-based clustering and structure-based antigen-antibody interfaces residues with per-site entropy and mutation rate profiling. These features enable in-depth exploration of antibody sequence characteristics, thereby facilitating functional and structural insights for rational antibody design. Together, by bridging antibody sequence, structure and function, MAAD offers an open and standardized platform that advances comparative antiviral research and supports therapeutic antibody discovery.

bioinformatics↗

bioRxiv2025

When Brain Models Are not Universal: Benchmarking of Ethnic Bias in MRI-Based Cognitive Prediction Across Modalities

Predictive neuroimaging models promise precision medicine but risk exacerbating health inequities if they perform unevenly across ethnic/racial groups. Using the Adolescent Brain Cognitive Development data, we benchmarked ethnic/racial bias in models predicting cognitive functioning from 91 MRI phenotypes across four training strategies. Models trained on one ethnicity performed best within that group. Models trained on participants sampled without regard to ethnicity, a common practice, performed better on White participants, likely because the ABCD sample was predominantly White. Training on equal-sized White and African American subsamples reduced disparities without accuracy loss, emerging as the upper bound for both accuracy and fairness. Structural MRI exhibited the greatest bias, whereas task-based fMRI phenotypes were more equitable. Stronger brain-cognition associations generalized more equitably, but multimodal stacking--despite enhancing prediction--did not improve fairness. Increasing representation of African American participants improved performance up to balanced sampling, with diminishing returns beyond. This first modality-wide benchmark reveals pervasive, modality-dependent ethnic bias in cognitive prediction and identifies key factors shaping equity in neuroimaging models.

neuroscience↗

bioRxiv2025

End-to-end high-throughput single-cell proteomics via SPRINT and dual-spray LC-MS

Single-cell proteomics (SCP) enables direct measurement of protein heterogeneity but remains constrained by throughput and limited applicability to primary tissues. Here, we present an integrated workflow developed to address both challenges. We engineered SPRINT, an AI-powered bioprinting platform that prepares more than 10,000 single cells per day, over tenfold faster than existing systems, while maintaining stability and enabling identification of over 6,000 proteins from individual HeLa cells. To expand analytical capacity, we further designed a dual-spray tandem direct injection (TDI) LC system that parallelizes non-analytical steps with peptide separation, doubling MS utilization efficiency and enabling 168 label-free SCP runs per day without sensitivity loss. Together with optimized tissue-dissociation protocols, this integrated workflow enabled creation of the first mouse tissue-derived SCP atlas, profiling >1,000 single cells across six organs. These advances establish SCP as a scalable platform ready for broad biological and biomedical applications.

systems biology↗

bioRxiv2025

Design and Development of DNA Damage Chemical Inducers of Proximity (DD-CIP) for Targeted Cancer Therapy

Many chemotherapies are effective against cancers that display high levels of genome instability by disrupting or overwhelming the DNA damage response to induce cell death. PARP inhibitors (PARPi) exploit this vulnerability by stalling DNA repair particularly in homologous recombination (HR)-deficient cancer cells. Although PARPi are now used to treat BRCA1/2-mutated cancers such as ovarian and breast cancers, they are still limited to a narrow range of clinical indications and are susceptible to acquired resistance. Here, we introduce "DNA Damage Chemical Inducers of Proximity" (DD-CIPs), bivalent molecules that rewire the mechanism of action of conventional PARPi. The DD-CIPs function through chemical induced proximity between PARP1/2 and the chromatin remodeling protein, BRD4. From a candidate library of DD-CIPs, we identified DD-CIP1 which induces the DNA damage response (DDR) and apoptosis to a range of cancer lines at two-digit nanomolar concentrations. Further optimization yielded DD-CIP2, which induces tumor cell death at nanomolar concentrations across diverse blood and solid cancer cells, including cancer types that are insensitive to PARPi. Using small-cell lung cancer (SCLC) as a model, we found that DD-CIP2 triggers DDR, cell cycle arrest, and apoptosis in vitro, leading to anti-tumor efficacy without substantial toxicity in preclinical SCLC xenograft models at well tolerated doses. Our findings demonstrate that DD-CIPs may provide an opportunity to address the limitations of traditional PARPi and establish chemical induced proximity as a strategy for modulating the DDR in cancer.

cancer biology↗

bioRxiv2025

alpseq: an open-source workflow to turbocharge nanobody discovery with high-throughput sequencing

Nanobodies have emerged as promising tools for many biotechnological applications due to their small size, high stability, and remarkable binding specificity. Next-Generation Sequencing (NGS) enables deep profiling of large nanobody libraries and panning campaigns, however the scale and diversity of nanobody NGS datasets presents a significant bioinformatic challenge. To this end, we have developed alpseq, an optimised, open-source software pipeline designed specifically for the efficient and accurate processing of NGS data from nanobody libraries and panning campaigns. alpseq is also paired with a PCR-free sequencing library preparation protocol to allow researchers to easily generate their own data while avoiding biases. The alpseq software pipeline is composed of two parts: a pre-processing module written in Nextflow efficiently handles raw nanobody reads in a single line of code. These results are then fed into the analysis module, which contains a comprehensive suite of functions for quality control, diversity analysis, identification of enriched sequences and clustering. alpseq also creates a user-friendly interactive report which empowers scientists to explore their data without the need for extensive bioinformatic experience. Sophisticated panning campaign designs are supported, such as replicates and comparisons between different pans to find cross-binding leads. alpseq thus generates insights into the nanobody selection process and delivers a list of lead candidates for further experimental validation and downstream applications. alspeq is available at https://github.com/kzeglinski/alpseq.

bioinformatics↗