Biology subjects

Lan, Y.

Publications and source records attributed to Lan, Y..

At least 19 recordsLinked to original sources

bioRxiv2026

EISCA and EISTA: Full-Spectrum Pipelines for Single-Cell and Spatial Transcriptomics Analysis

Single-cell and spatial transcriptomics are transforming our understanding of cellular heterogeneity and tissue organization, yet their analytical complexity remains a major bottleneck. Here, we present EISCA and EISTA, two standardized, end-to-end pipelines for single-cell RNA-seq and imaging-based spatial transcriptomics analysis. Built on the Nextflow nf-core framework, both pipelines implement modular, scalable, and reproducible workflows spanning primary, secondary, and tertiary analyses, from raw data processing to advanced downstream analyses. EISCA supports droplet- and plate-based scRNA-seq technologies, while EISTA is tailored for high-resolution spatial platforms including Vizgen MERFISH and 10x Xenium. Together, they integrate state-of-the-art methods for quality control, normalization, clustering, integration, cell-type annotation, differential expression, and cell-cell communication, with EISTA further enabling spatial statistical analyses. A central design principle is to balance standardization with flexibility: workflows can be executed end-to-end or modularly, enabling iterative, exploratory analyses with minimal overhead. Both pipelines deliver rapid preliminary results alongside an out-of-the-box report, facilitating immediate data assessment and accelerating downstream discovery. Case studies in plant immunity and human sepsis demonstrate that EISTA and EISCA reproducibly can be used to recover biologically meaningful insights. Collectively, these pipelines provide efficient, flexible, and scalable solutions for comprehensive single-cell and spatial transcriptomics analyses.

bioinformatics↗

bioRxiv2026

Barcoded-Plasmid DNA library construction for recording cell lineage trees enabled by a Scalable and modular Biofoundry-based Automated Robotic Pipeline

High-quality plasmid DNA purification at high throughput remains a significant bottleneck in molecular biology and bioengineering. Current methods frequently fail to deliver sufficient yields of pure, transfection-grade DNA required for genetic engineering applications in mammalian cells. Here, we present a Biofoundry-based automated pipeline using the CyBio FeliX robotic liquid handling platform to rapidly purify plasmid DNA with minimal manual intervention. The protocol leverages Solid Phase Reversible Immobilisation (SPRI)-based magnetic bead technology to ensure consistency, scalability, and DNA purity suitable for downstream viral particle production and mammalian cell transfection. The pipeline supports flexible processing of between 8 and 96 samples per run, making it adaptable across a wide range of experimental scales. The protocol is openly available via Earlham Institute GitHub repository, enabling broad adoption across the bioscientific community and contributing to the growing toolkit of reproducible, scalable engineering biology workflows. In this work, we employed an integrated robotic pipeline to process 528 pooled DNA plasmids and built a Lentiviral DNA plasmid library for lineage tracing, validated the library by sequencing, and demonstrated efficacy in downstream mammalian cell transfection experiments.

synthetic biology↗

bioRxiv2026

Internalized Components of Membrane Attack Complexes Disrupt Proteostasis and Acquire Alarmin-Like Properties

Immune effects of membrane attack complexes (MAC) have been widely attributed to their abilities to cause cell death. Here, we show that the MAC component, C9, forms non-cytolytic aggregates with pro-inflammatory effects. Intracellular aggregates of C9 are detected within inflamed tissues of patients in association with endothelial cell (EC) activation but not increased cell death. We identify NUMBL as a Rab35 effector that directly binds surface-bound C9 to promote C9 internalization and entry into the endolysosomal pathway. Within acidified endolysosomes, C9 forms insoluble aggregates that are targeted for degradative aggrephagy in a process that activates NF-{kappa}B. For C9 aggrephagy to occur, ZFYVE21, a Rab5 effector, complexes with RNF34 to bridge C9 aggregates to LC3B+ aggresome membranes. We detect C9 aggregates in vivo, and we show that a ZFYVE21-RNF34 signaling axis is required for C9 aggrephagy and NF-{kappa}B -dependent EC activation in three separate mouse models. Mice with conditional loss of ZFYVE21 in ECs show reduced aggregraphy, resulting in attenuated systemic inflammation and reduced tissue injury following skin transplantation. Our data show that the C9 component of MACs forms intracellular aggregates with alarmin-like properties.

biochemistry↗

bioRxiv2026

Evolutionary Rewiring of Root Cell-Type Nitrogen Responses

Nitrate induces widespread transcriptional reprogramming in plants, but how transcription factors coordinate these responses across cell types and evolve across species with divergent root patterning remains unclear. Using single-cell transcriptomics in Arabidopsis thaliana and Solanum lycopersicum (tomato) under varying nitrate conditions, we demonstrate that many transcriptional responses are cell type and developmental stage-specific. Arabidopsis NIN-LIKE PROTEIN 7 (NLP7) directly modulates both broadly expressed and cell-type-specific targets, but is not required to establish the cell type specific nature of these patterns. Comparative analysis reveals evolutionary shifts; key nitrogen programs are localized to different cell types in tomato, with the exodermis as a major regulatory hub. Identification of tomato NLP7a and NLP7b direct targets at cellular resolution supports their role in direct modulation of cell type N-transcriptional programs. This is evidenced by NLP7-controlled exodermal cell wall remodeling in low nitrate, highlighting functional conservation despite divergent cellular topographies.

plant biology↗

bioRxiv2026

GalaxyVS: Exploring 100-Billion Compounds in Seconds

We present GalaxyVS, a hardware-software co-designed virtual screening framework built to explore the 100-billion commercially accessible chemical space in seconds, deployed at the National Supercomputing Center in Tianjin. Built upon the dense vector retrieval paradigm of DrugCLIP, GalaxyVS bypasses the structural dependencies and computational overhead of classical docking to enable rapid screening against experimentally determined as well as geometrically feasible pockets on AlphaFold-predicted structures. To scale this paradigm to the 100-billion level, the system must overcome the significant computational burden of offline representation encoding, critical memory and I/O bottlenecks during online retrieval, and the risks of diversity collapse and precision loss within final screening results. Utilizing the heterogeneous supercomputing infrastructure, GalaxyVS accelerates the offline encoding through deep operator adaptations and resolves online retrieval bottlenecks via disk-native vector indexing coupled with in-memory staging to ensure both broad accessibility and high throughput. Concurrently, a two-stage refinement protocol effectively mitigates diversity collapse and ensures high-fidelity affinity ranking. Consequently, GalaxyVS achieves a daily scoring throughput of 1.5 x 1016 target-ligand pairs, representing a six-orders-of-magnitude leap over previous supercomputing records. Driven by this throughput, we screened nearly 100,000 protein structures across six species against the 100-billion compound library in just 16 hours. The resulting comprehensive cross-species interaction landscape, GalaxyDB, will be openly released at https://galaxyvs.drugclip.com.

bioinformatics↗

bioRxiv2026

A comparison of scalable approaches for the pairwise analysis of large pathogen genomic and spatial datasets: an application to studying Mycobacterium tuberculosis transmission

Pairwise analysis of genomic and spatial data offers opportunities to identify and estimate the associations between covariates and the transmission of pathogens between individuals. However, such pairwise analyses are computationally intensive, and may not be feasible to conduct given the high dyad count in even moderately sized datasets. Here we compare two approaches to increase the efficiency of pairwise analysis for large datasets. We quantify and compare the performance of divide-and-conquer Bayesian model fitting and pairwise case-control approaches for estimating associations between individual- and pair-level covariates and shared membership in a transmission cluster. We utilize a large dataset (n=4,154) of spatially-referenced, genomically-sequenced Mycobacterium tuberculosis isolates collected from a single city for this analysis. We find that the case-control approach produces unbiased estimates of effect sizes with expected credible interval coverage and is more robust than the divide-and-conquer method when effect sizes are large. Thus, we recommend using the case-control approach with at least three controls per case to downscale datasets for pairwise analysis when analysis of the entire dataset is not possible. This approach mitigates the computational challenges of pairwise Bayesian modeling on datasets that require significant computational resources while maintaining desired inferential properties. Author SummaryPairwise analyses of large datasets to study pathogen transmission are computationally demanding because they typically require simultaneous analysis of each possible pair of individuals in a dataset; as datasets become larger these analyses often are not feasible to conduct even with access to high-performance computing resources. In this work, we compare a case-control approach and divide-and-conquer approaches for more efficient pairwise analysis of large datasets. Using a large dataset of Mycobacterium tuberculosis isolates including genetic and spatial data, we investigate the performance of each method for estimating the associations between host covariates and genetic clustering of isolates. We find that the case-control approach is generally preferred over methods which first divide the data into subsets and then combine results. While additional extensions of these analyses are needed to test the generality of these findings to other data settings, this work provides a practical way forward for the pairwise analysis of large datasets to study pathogen transmission.

microbiology↗

bioRxiv2026

Physiological architecture and evolutionary origins of cellular adaptability

The capacity to adapt to complex environments ( adaptability) is a defining property of cells. Yet, its relationship across single-cell physiology, population-level responses, and evolutionary timescales remains unclear. We performed single-cell transcriptional profiling of budding yeast across 20 complex environments before and after long-term selection for increased osmotolerance. In ancestral populations, transcriptional responses organized into a reproducible hierarchy where adaptation to certain cues took precedence over others. This hierarchy mechanistically originated within individual cells through contingent regulation of translation initiation. Evolution for >3,000 generations under osmotic stress increased osmotolerance while reordering this hierarchy, selectively deprioritizing osmotic stress as an organizing axis of adaptation. The derived strain exhibited impaired integration of stress responses, defective translational coordination, and reduced fitness outside the selected condition. Together, these findings illustrate that cellular adaptability is an evolved architecture whose form is set by the history of selection. One Sentence SummaryCells adapt to complex environments via a prioritization hierarchy; this hierarchy is shaped by the frequency and magnitude of environmental fluctuations in their evolutionary history.

cell biology↗

bioRxiv2026

Mitotically Driven Cytoskeletal Reorganization Governs Zebrafish Left-Right Organizer Detachment from EVL and Lumen Morphogenesis

Left-right asymmetry in vertebrate embryos is established by the left-right organizer (LRO), with the zebrafish Kupffers vesicle (KV) providing a tractable model for studying de novo epithelial morphogenesis. During KV formation, dorsal forerunner cells (DFCs) initially form polarized attachments to the enveloping layer (EVL) before reorganizing into multicellular rosettes that precede lumen formation. Here, we show that while DFC-EVL junctions form independently of mitosis, early cytokinetic events play an instructive role in remodeling these contacts. Live imaging and targeted laser ablation reveal that cytokinetic bridges and their associated microtubule bundles recruit actin, seed rosette centers, and promote the transition from external to internal epithelial organization. Disruption of early, but not later, DFC divisions impairs actin accumulation, rosette coalescence, KV detachment from the EVL, and lumenogenesis. These findings identify a temporally restricted role for cytokinesis in organizing cytoskeletal architecture and reveal how division history directs epithelial tissue assembly during LRO development.

cell biology↗

bioRxiv2026

Unravelling genome-wide mosaic microsatellite mutations at single-cell resolution

Short tandem repeats (STRs), or microsatellites, are highly mutable genomic elements that modulate gene regulations and are implicated in a range of human diseases. However, detecting mosaic STR mutations at single-cell resolution remains challenging due to both technical and biological complexities. To address this, we developed BayesMonSTR, a robust algorithm that enables accurate detection of mosaic STR mutations. Using this tool in single-cell analysis of human tissues, we reveal an accumulation of longer mosaic STR insertions and deletions (indels) in aging mitotic and post-mitotic cells. Strikingly, prefrontal cortex (PFC) neurons accumulate a higher burden of STR mutations than B cells or lung epithelium, with aged neurons exhibiting a particularly pronounced increase in longer STR deletions. These mutations are enriched at transcription start sites (TSSs) and active enhancers of highly expressed genes. Our work establishes a foundation for genome-wide, hypothesis-free discovery of disease-associated mosaic STR mutations and reveals a previously unexplored landscape of mosaic STR variation in development and aging.

bioinformatics↗

bioRxiv2026

Beyond Lipschitz: Ranking Binding Affinity in Hyperbolic Space

Despite the importance of protein-ligand affinity ranking in drug discovery, existing deep learning models struggle to distinguish hard inactives that are structurally similar to active compounds but biologically inactive. We theoretically show this failure stems from the Lipschitz continuity constraint in Euclidean space, which makes neural networks locally insensitive to subtle yet critical structural perturbations. To overcome this, we propose AlphaRank, which demonstrates that scoring affinity as the negative hyperbolic geodesic distance allows subtle tangential variations to bypass Lipschitz constraints, intuitively corresponding to pivotal binding mode factors such as conformational fit. Meanwhile, AlphaRank employs joint optimization to simultaneously ranking affinity scores among actives in a proximity-aware manner and separate actives from decoy inactives through a geometric cone constraint. Experiments demonstrate that AlphaRank outperforms state-of-the-art models like Boltz-2 in both affinity ranking and active-inactive discrimination, while providing a interpretable representation space.

bioinformatics↗

bioRxiv2025

Autonomous liquid-handling robotics scripting through large language models enables accessible and safe protein engineering workflows

Laboratory automation accelerates synthetic biology, but robot programming remains a barrier to broader participation. Large language model (LLM)-based automation must address four interconnected requirements in this field: sequence-level biosecurity, community standards, context-dependent biological behavior, and distributed collaboration across laboratories. Here we present LabscriptAI, an execution-aware agent framework that couples authoring and runtime control loops to translate natural-language instructions into platform-specific robot scripts and support bounded recovery under deterministic authorization and human oversight. On a 90-task liquid-handling benchmark, LabscriptAI achieved a 96.7% simulation-pass rate, the highest among the evaluated LLM-based and commercial baselines. We demonstrated its capabilities through sequence screening at designated workflow checkpoints; standardized cell-free characterization of 854 green fluorescent protein (GFP) designs from 171 student teams, coupled to data deposition; containment-aware engineering of a formaldehyde-converting enzyme, yielding 6.7-fold higher ethylene glycol titers in a two-stage cascade; and quality-assured preparation of 531 genetic parts for the 2025 iGEM Distribution Kit distributed worldwide. Together, these results establish LabscriptAI as a biosecurity-aware, community-integrated framework connecting protocol authoring, controlled execution, and traceable experimental outputs across laboratory platforms.

bioengineering↗

bioRxiv2025

A comparison of long-read single-cell transcriptomic approaches

Long-read sequencing enables the incorporation of isoform-level expression into single-cell transcriptomic studies, offering detail beyond those accessible with short-read methods. Although insightful, these approaches have typically been costly and yielded limited data for each individual cell. Recent advances in library preparation approaches and sequencing throughput have brought long-read single-cell studies closer to the mainstream. Here, we present a comparative analysis of commercial approaches for single-cell long-read sequencing. We have performed parallel analyses of the same cDNA material, generated using the 10X genomics platform, on Illumina short-read, and PacBio and Oxford Nanopore long-read platforms. We also demonstrate the impact of CRISPR-based depletion of libraries, to remove highly expressed transcripts, prior to long-read sequencing in these experiments. By analysing single-source cDNA libraries in parallel, we enable a direct comparison of each platform, evaluating standard metrics alongside concordance in clustering and cell type identification. While each approach generates usable gene and isoform expression data, we identify limitations common across platforms, primarily linked to cDNA synthesis inefficiencies and read filtering strategies. Our work demonstrates the increasing utility of single-cell long-read sequencing for isoform-resolved analyses, such as direct immunoglobulin chain reconstruction without additional amplification, and the detection of alternative splicing patterns across immune cell subtypes in CD45, a key gene for immune cell activation and differentiation. Our benchmarking of current platform options provides a foundation for researchers looking to adopt single-cell long-read sequencing into their transcriptomic studies, providing a framework for its integration into diverse biological questions.

cell biology↗

bioRxiv2025

How Good is AlphaFold3 at Ranking Drug Binding Affinities?

Accurate affinity ranking of small molecules is pivotal for drug discovery. We investigate whether structure prediction models like AlphaFold3, pretrained on protein-ligand interactions, can address this task. Zero-shot evaluation of Protenix (an AlphaFold3-like model) demonstrates superior prioritization of active compounds over conventional scoring functions and state-of-the-art deep learning models. By further fine-tuning Protenix on structure-agnostic protein-ligand bioactivity data from ChEMBL and BindingDB, we develop AlphaRank that predicts pairwise affinity relationships. AlphaRank achieves prediction accuracy comparable to computationally intensive free energy perturbation (FEP+) workflows on standard benchmarks, while requiring substantially less computational resources. Our findings highlight the emergent potential of AlphaFold3-derived models in affinity ranking tasks and emphasize the necessity for targeted methodological exploration to fully harness their capabilities in drug discovery applications.

bioinformatics↗

bioRxiv2025

A nasal vaccine candidate, based on S2 and N proteins from SARS-CoV-2, generates a broad antibody response systemic and at lower respiratory tract.

Since the beginning of the COVID-19 pandemic various groups around the world have intensively worked in the development of vaccine candidates against SARS-CoV-2. Several vaccines have been approved in the past years, the majority are based on the Spike or RBD proteins and employs parenteral administration routes. Considering the recent history of Coronavirus zoonotic events, causing serious human health problems, the generation of vaccines with a broad scope of protection and the potential to cut / reduce the transmission remains in the spotlight. The current global pandemic preparedness initiatives have promoted also the preclinical evaluation of a new group of Coronavirus vaccines. In the present work a nasal vaccine candidate based on two highly conserved Sarbecovirus proteins, S2 and nucleocapsid (N), is evaluated in two different mice strains. The vaccine preparation, containing a CpG ODN as adjuvant, was able to generate high antibody titers against both antigens, in sera and bronchoalveolar lavages. This antibody response results cross-reactive to S2 from SARS-CoV-1 and MERS-CoV, and to N from SARS-CoV-1. However, a very low neutralizing capacity was found in the sera of the immunized mice when a pseudoviral system assay was used. On the other hand, the vaccine preparation induces, at systemic compartment, IFN {gamma} secretion, and a marked IgG2a response, specific against both proteins; a profile consistent with the development of a Th1 pattern. Although further evaluations should be done, including protection assays, the demonstrated cross-reactivity level and mucosal response constitutes promising features of this vaccine candidate.

immunology↗

bioRxiv2025

Exosomal miR-145-5p promotes apoptosis of renal tubule epithelial cells through the JNK signalling pathway

Acute kidney injury (AKI) is the most prevalent extrapulmonary organ failure observed in patients with acute respiratory distress syndrome (ARDS). However, the underlying mechanisms leading to AKI in ARDS remain unclear. Exosomes facilitate intercellular and inter-organ communication via exosomal microRNAs (miRNAs), which potentially contribute to the crosstalk between the lung and kidney in ARDS. Therefore, the purposes of our study were to investigate the involvement of exosomal miRNAs in the development of AKI in ARDS. In current study, Sprague-Dawley rats were induced to develop ARDS via hydrochloric acid aspiration. Nanoparticle tracking analysis, Transmission electron microscopy and western blotting were conducted to analyse the exosomes. Quantitative reverse transcription-polymerase chain reaction and in situ hybridisation showed elevated expression level of miR-145-5p in the circulating exosomes and lung tissue of ARDS rats. Overexpression of miR-145-5p disrupted the proteomic profile of renal proximal tubule epithelial cells, leading to impacts on proteins associated with various biological processes, including apoptosis. We further demonstrate that miR-145-5p overexpression enhanced JNK phosphorylation by directly repressing RBM3 expression and promoted cell apoptosis. In vivo, administration of agomiR-145-5p injection via intravenous route, which subsequently induced renal tubular cell apoptosis. These findings have revealed that miR-145-5p may serve as a novel mediator for the apoptosis of renal cell in the context of ARDS.

cell biology↗

bioRxiv2024

A Comparison of Nanopore Data Types, Basecalling, and Assembly Algorithms: Whole Plant Genome Assembly and Methylation Analysis from a Single MinION Flow cell

BackgroundOxford Nanopore Technologies MinION sequencer is a compact, USB-powered device that has been available since 2014. Since the release of the earliest model, the throughput and error rate of the platform have improved dramatically and it has become possible to consider sequencing and assembly of eukaryotic organisms using a single nanopore flow cell. Here, we present MinION sequence data, assembly and methylation analysis for the Columbia (Col-0) accession of the model plant Arabidopsis thaliana. Further, we demonstrate the effect that recent developments (specifically the Q20+ chemistry and basecaller improvements) have had on read accuracy and assembly quality. FindingsDNA extracted from leaves of A. thaliana Col-0 was sequenced on two MinION flow cells, together amounting to 115x coverage of the 135 Mb genome. Reads were assembled and polished using several bioinformatics pipelines for nanopore sequence data. We performed CpG methylation analysis directly from the nanopore data, and this correlated well with a previously published bisulfite dataset. ConclusionsOur results suggest that a single MinION flow cell can generate sufficient data to assemble a genome of 135Mb, indicate functional elements, and unlock genomics for researchers of novel species from $600 in under 5 days. Here we release the full nanopore read sets for both flow cells, together with the nanopore-only assemblies. The data presented here is expected to be of particular interest to those in the Arabidopsis community, designing genome projects (with functional annotations), expanding our understanding of epigenetics across the tree of life, and for designing university or high school practicals teaching genomics and epigenomics. HighlightWe evaluate the effect of recent nanopore chemistry and software improvements on the ability to assemble a complete genome and conduct methylation analysis using data from a single MinION flow cell.

genomics↗

bioRxiv2024

TiFM2.0 - Versatile mechanical measurement and actuation in live embryos

During development, spatial-temporally patterned tissue-level stresses and mechanical properties create diverse tissue shapes. To understand the mechanics of small-scale embryonic tissues, precisely controlled sensors and actuators are needed. Previously, we reported a control-based approach named tissue force microscopy (TiFM1.0), which combines dynamic positioning and imaging of an inserted cantilever probe to directly measure and impose forces in early avian embryos. Here we present an upgraded system (TiFM2.0) that utilises interferometer positioning to minimise probe holder footprint, enhancing accessibility and imaging signal. This new design enables a double-probe configuration for bidirectional stretching, compression and stress propagation experiments. As proof-of-concept, we showcase a variety of examples of TiFM2.0 applications in chicken and zebrafish embryos, including the characterization of mechanical heterogeneities important for the morphogenesis of the chicken posterior body axis. We also present simplified designs and protocols for the replication of TiFM systems with minimal custom engineering for developmental biology labs.

developmental biology↗

bioRxiv2024

Nitrate Promoting Hair Growth Through Enhancing Wnt/β-catenin and Downregulating TGF-β/Samd/BMP signaling pathway in C57BL/6 Mice

Nitrate found widely distributed in vegetables and fruits. It has been proved to have many physiological functions through nitrate-nitrite-NO pathway, such as attenuate oxidative stress, enhance the skeletal muscle and tone skin vascular. But the function of nitrate in regulating hair growth is unclear. Here, we evaluated potential roles of nitrate on hair growth and its mechanisms by in vivo models. Mice treated with 10mM nitrate supplementation significantly promoting hair growth, along with an increase in the number of hair follicles and thickness of dermis. We further demonstrated that Wnt3a and {beta}-catenin were highly expressed in the nitrate-treated groups, while the expression of TGF-{beta}1 was attenuated. Moreover, nitrate also increased the expression levels of growth factors (Fgf, Igf-1, and Vegf). Our data indicated that nitrate supplementation could effectively accelerate hair regrowth through activating the Wnt/{beta}-catenin and inhibiting TGF-{beta}1/Smad/BMP signaling pathway. Therefore, these findings provide support for a potential therapeutic role of nitrate in preventing and treating alopecia.

biochemistry↗