Biology subjects

Chi, S.

Publications and source records attributed to Chi, S..

18 recordsLinked to original sources

bioRxiv2026

SPATNIC enables H&E-grounded cancer-cell identification across spatial and single-cell transcriptomic modalities in colorectal cancer

Tumors contain diverse non-cancer cells that constitute the tumor microenvironment; thus, identifying which cells are cancerous is a foundational and critical first step in cancer research. However, to our knowledge, no transcriptome-based classifier has been trained using pathologist-defined annotations from matched H&E images at single-cell resolution. Moreover, few tools can be shared across diverse platforms. Here, we present SPATNIC, a variational autoencoder that classifies individual cells as cancer or normal, trained on Xenium cells labeled by a pathologist review of matched H&E images. It matches or exceeds the performance of existing tools in primary colorectal tumors and liver and lung metastases. SPATNIC generalizes zero-shot across Visium, Visium HD, CosMx, Xenium, MERSCOPE, and single-cell RNA sequencing. In public single-cell atlases, it identified a rare POU2F3 tuft-like cancer stem cell population missed by existing annotations and associated with worse survival. In rectal cancer profiled before and after chemoradiotherapy, it separated cancer from normal epithelium in tissue, thus enabling per-patient screening of antibody-drug conjugate (ADC) targets.

cancer biology↗

bioRxiv2026

WrapEvoFS enables auditable feature compression with regret-constrained representative locking

WrapEvoFS provides a deterministic, auditable rule for choosing one feature set from repeated stochastic searches. It retains candidates within a prespecified development-score gap, selects the most representative set by feature overlap, and records the decision before held-out evaluation. A corrected genetic objective preserves score ordering lost through zero truncation. In a fully nested four-class cancer analysis, all 30 locked signatures met the 0.01 tolerance, and 11 differed from the highest-scoring candidate; every changed selection increased mean feature overlap within the eligible pool. Paired differences in area under the receiver operating characteristic curve against recursive feature elimination with cross-validation all had 95% intervals crossing zero. No selector dominated matched comparisons on feature count and predictive performance, and one-time evaluation of frozen signatures at held-out centers yielded heterogeneous effects. WrapEvoFS standardizes selection within a fixed candidate bank under an empirical score-gap constraint, enabling reproducible reporting of performance, compression, and feature-agreement trade-offs without implying predictive superiority or stability under changes in participant composition.

bioinformatics↗

bioRxiv2026

A shark variable new antigen receptor recognizes an occluded epitope of fibroblast activation protein

Variable new antigen receptors (VNARs) are the smallest naturally occurring antibody binding domains. Their size allows VNARs to access sterically restricted epitopes that are inaccessible to conventional antibodies. We recently identified a suite of VNARs that target fibroblast activation protein (FAP), a stromal serine protease indicative of extracellular matrix remodeling. The presence of FAP on the surface of cancer-associated fibroblasts (CAFs) that promote immunosuppression has made FAP a compelling therapeutic target for cancer therapy. Although antibodies targeting FAP have been developed, there is a paucity of information on how biologics engage FAP. Here, we used single-particle cryogenic electron microscopy (cryo-EM) to compare FAP recognition of three antibody architectures: a shark-derived VNAR, variable heavy (VH) and light domains (VL) of a humanized Immunoglobulin G (IgG), and a camelid-derived VHH. The humanized VH-VL domains and camelid VHH both target a solvent-exposed {beta}-propeller domain, whereas the VNAR binds a highly conserved, topologically recessed epitope at the FAP dimer interface. Radical-footprinting mass spectrometry (MS) further mapped two additional immune-derived VNARs to distinct FAP surfaces outside the shared {beta}-propeller epitope. These findings demonstrate how unique VNAR architecture can expand access to underexplored FAP surfaces and establish a structural framework for rational multiepitope targeting strategies.

biochemistry↗

bioRxiv2026

Recovering biological structure in sparse single-cell proteomics with GIRAFI

Single-cell proteomics (SCP) based on liquid-chromatography mass-spectrometry resolves protein-level cellular heterogeneity, but interpretation remains limited by detection-linked sparsity. SCP profiles continuous, peptide-derived intensities and has lower throughput than single-cell RNA sequencing, making denoising methods for large-scale, count-based transcriptomics difficult to apply. Here we present GIRAFI, a graph-informed statistical learning framework that imputes missing values and reveals reproducible cell states by constraining inference to dataset-aware, prior-knowledge-informed protein neighborhoods. We evaluated GIRAFI across SCP datasets spanning diverse biological/technical contexts. In masking-based recovery experiments and cell-type-specific protein-protein interaction inference, GIRAFI outperformed existing methods, and matched bulk proteomics comparisons corroborated recovery accuracy and ablations supported the graph-informed design. Beyond reduced replicate- and source-associated technical structure, GIRAFI recovered ground-truth cell-type annotations, improved cell state-resolved pathway analysis, and enabled trajectory inference consistent with known time courses. These results establish graph-constrained imputation as an effective strategy for improving SCP robustness, biological structure, interpretation, and cross-dataset comparability.

bioinformatics↗

bioRxiv2026

Dysregulated tissue-resident lymphocytes drive age-associated emphysema by impairing alveolar regeneration

Aging is often associated with progressive tissue degeneration and chronic inflammation, yet the role of immune cells in mediating structural and functional decline in organs remains poorly defined. Here, we investigated immune-tissue interactions in the aged lung and identified emphysematous remodeling characterized by alveolar loss. Notably, aged lungs exhibited a marked expansion of tissue-resident lymphocytes (TRLs) with senescent features, accompanied by a significant reduction in alveolar stem/progenitor cell (AT2) abundance. In vivo adoptive T cell transfer and 3D immune-stem cell organoid assays revealed that these expanded TRLs suppressed AT2 growth via secretion of oncostatin M and interferon gamma. In vivo blockade of IL-7 receptor (IL-7R) reduced TRL accumulation in the lungs and ameliorated age-related emphysematous changes, including restoration of alveolar density. Our findings identify TRLs as key drivers of alveolar degeneration in aging and propose IL-7R inhibition as a therapeutic strategy to mitigate pulmonary decline. TeaserBlocking IL-7R clears harmful lymphocytes and helps rebuild the damaged air sacs of the aging lung.

cell biology↗

bioRxiv2026

Bridging Simplicity and Depth in Single-Cell Proteomics: A Cost-Effective Workflow and Expanded Framework for Data Evaluation

Single-cell proteomics (SCP) offers direct insight into functional protein states that drive cellular heterogeneity, complementing genomic and transcriptomic analyses. Although recent reports have demonstrated improved proteome coverage, their reliance on specialized instrumentation limits broader adoption. Additionally, current evaluation practices remain largely centered on protein and peptide identification counts, which alone do not fully reflect data quality or biological interpretability. Here, we describe an accessible, label-free SCP workflow which implements easily accessible laboratory equipment: a single-cell dispenser, conventional multiwell plates, and an incubator with water-bath-based humidity control. Using trapped ion mobility spectrometry-time-of-flight mass spectrometry (timsTOF), we systematically optimize key sample preparation variables, including trypsin concentration, incubation time, reduction/alkylation, digestion conditions, and plate types, which together maximize data quality and reproducibility. We further introduce a data-quality framework that moves beyond identification counts, emphasizing quantitative consistency and biological interpretability via individual protein coverage completeness across cells, coefficients of variation across technical replicates, peptide-to-protein ratios, and single-cell-to-bulk correlations. Collectively, our approach lowers technical barriers to accessing SCP while enabling more rigorous, interpretable, and scalable SCP analysis across diverse research contexts.

biochemistry↗

bioRxiv2025

MELODY: Mediation Analysis in Logistic Regression for High-Dimensional Mediators and a Binary Outcome

Mediation analysis is a pivotal tool for elucidating the indirect effect of an environmental factor or treatment on disease through potentially high-dimensional omics data, such as gene expression profiles. However, traditional mediation analysis methods tailored for binary outcomes often rely on the rare disease assumption in logistic regression and provide inadequate measures of total mediation effect when multiple mediators have effects in different directions. In this paper, we develop a MEdiation analysis framework in LOgistic regression for high-Dimensional mediators and a binarY outcome (MELODY). It leverages a second-moment-based measure analogous to the R2 for linear models to quantify the total mediation effect. We also develop a variable selection procedure for high-dimensional data to reduce bias introduced by non-mediators. Our comprehensive simulations demonstrate the superior performance of MELODY in scenarios with non-rare disease binary outcomes and high-dimensional mediators. We apply MELODY to the Framingham Heart Study of over 5000 individuals to analyze the mediation effects of metabolomics and transcriptomics data on the pathways from sex to incident coronary heart disease.

genomics↗

bioRxiv2025

SPIN: Inkjet-Driven Nanowell Workflow for Scalable and Sensitive Single-Cell Proteomics

Single-cell proteomics is emerging as a powerful approach to resolve cellular heterogeneity, yet sample processing remains challenging due to limited input material and the absence of protein amplification. We present a protocol centered on an image-guided, machine-learning-driven inkjet single-cell printer integrated with a dew-point-controlled nanowell chip to reduce loss, increase throughput, and improve reproducibility. The system dispenses single cells at >1 Hz into sealed nanoliter wells with minimal surface contact, virtually eliminating evaporation; a high-thermal-conductivity aluminum substrate and precise environmental control further ensure exceptional reproducibility. Relative to a commercial dispenser, the workflow yields significantly higher protein and peptide recovery without bias toward high-abundance species, delivering uniformly deep coverage. Biological pathway analysis emphasizes the robustness of this workflow, as there is a near 100% protein completeness detected among the enzymes in the Kreb cycle in both A549 and astrocytes, suggesting the consistency across all samples evaluated. This platform addresses core processing bottlenecks and enables reliable, scalable single-cell proteomics.

systems biology↗

bioRxiv2024

Benchmarking Recent Computational Tools for DNA-binding Protein Identification

Identification of DNA-binding proteins (DBPs) is a crucial task in genome annotation, as it aids in understanding gene regulation, DNA replication, transcriptional control and various cellular processes. In this paper, we conduct an unbiased benchmarking of eleven state-of-the-art computational tools as well as traditional tools such as ScanProsite, BLAST, and HMMER for identifying DBPs. We highlight the data leakage issue in conventional datasets leading to inflated performance. We introduce new evaluation datasets to support further development. Through a comprehensive evaluation pipeline, we identify potential limitations in models, feature extraction techniques and training methods; and recommend solutions regarding these issues. We show that combining the predictions of the two best computational tools with BLAST based prediction significantly enhances DBP identification capability. We provide this consensus method as user-friendly software. The datasets and software are available at: https://github.com/Rafeed-bot/DNA_BP_Benchmarking. 1. Key PointsO_LIWe designed a comprehensive evaluation pipeline which systematically evaluates eleven recent machine learning (ML) based DBP identification tools. C_LIO_LIWe analyzed the test prediction mistakes made by top-performing tools identifying their potential limitations in terms of model architecture, feature extraction and class balancing. C_LIO_LIWe showed that although the best of these tools do not convincingly outperform BLAST, they still provide substantial value when integrated together with BLAST into a simple majority-voting ensemble. C_LIO_LIWe provide recommendations on more robust development & evaluation and better usability of future tools. C_LIO_LIWe provide the two best-performing ML-based tools, BLAST and the ensemble method as user-friendly software, as well as our proposed datasets, publicly available via GitHub. C_LI

bioinformatics↗

bioRxiv2024

A diverse landscape of FGFR alterations and co-mutations defines novel therapeutic strategies in pediatric low-grade gliomas

Alterations in Fibroblast growth factor receptor (FGFR)-family proteins frequently occur as oncogenes in many cancers, including a subset of pediatric gliomas. Here, we performed a genomic analysis of 11,635 gliomas across ages and found that 4.5% of all gliomas harbor FGFR alterations including structural variants (SV) and single nucleotide variants (SNV), with an incidence of almost 10% in pediatric gliomas. FGFR family members are differentially enriched by age, tumor grade, and histological subtype, with FGFR1-alterations associated with glioneuronal histologies and pediatric low-grade gliomas. Across development, we find FGFR1 expression in both neuronal and glial precursors, while FGFR3 expression is largely restricted to astrocytic lineages. Leveraging novel isogenic model systems, we confirm FGFR1 alterations to be sufficient to activate MAPK and mTOR signaling, drive gliomagenesis, activate neuronal transcriptional programs and exhibit sensitivity to MAPK pathway inhibitors, including pan-FGFR inhibitors. Models driven by FGFR1 SVs exhibited different patterns of sensitivity compared to those driven by SNVs. Finally, we performed a retrospective analysis of clinical responses in children diagnosed with FGFR-driven gliomas and found that targeted MAPK or FGFR-inhibition with currently available inhibitors is largely associated with stability of disease. This study provides key insights into the biology of FGFR1-altered gliomas, therapeutic strategies to target them and associated challenges that still need to be overcome.

cancer biology↗

bioRxiv2024

Spatial domain analysis to estimate spatiotemporal pathological mechanisms in microenvironment with single-cell spatial omics data

Single-cell spatial omics analysis requires consideration of biological functions and mechanisms in a microenvironment. However, microenvironment analysis using bioinformatic methods is limited by the need to detect histological morphology. In this study, we developed SpatialKNife (SKNY), an image-processing-based toolkit that detects spatial domains that potentially reflect histology and extends these domains to the microenvironment. The SKNY algorithm identified tumour spatial domains from spatial transcriptomic data of breast cancer, followed by clustering of these domains, trajectory estimation, and spatial extension to the tumour microenvironment (TME). The results of the trajectory estimation were consistent with the known mechanisms of cancer progression. We observed endothelial cell and macrophage infiltration into the TME at mid-stage progression. Our results suggest that analysis using the spatial domain as a unit reflects pathological mechanisms in the TME. This approach may be applicable to the biological estimation of diverse microenvironments.

bioinformatics↗

bioRxiv2023

MASH: Mediation Analysis of Survival Outcome and High-dimensional Omics Mediators with Application to Complex Diseases

Environmental exposures such as cigarette smoking influence health out-comes through intermediate molecular phenotypes, such as the methylome, transcriptome, and metabolome. Mediation analysis is a useful tool for in-vestigating the role of potentially high-dimensional intermediate phenotypes in the relationship between environmental exposures and health outcomes. However, little work has been done on mediation analysis when the mediators are high-dimensional and the outcome is a survival endpoint, and none of it has provided a robust measure of total mediation effect. To this end, we propose an estimation procedure for Mediation Analysis of Survival outcome and High-dimensional omics mediators (MASH) based on sure independence screening for putative mediator variable selection and a second-moment-based measure of total mediation effect for survival data analogous to the R2 measure in a linear model. Extensive simulations showed good performance of MASH in estimating the total mediation effect and identifying true mediators. By applying MASH to the metabolomics data of 1919 subjects in the Framingham Heart Study, we identified five metabolites as mediators of the effect of cigarette smoking on coronary heart disease risk (total mediation effect, 51.1%) and two metabolites as mediators between smoking and risk of cancer (total mediation effect, 50.7%). Application of MASH to a diffuse large B-cell lymphoma genomics data set identified copy-number variations for eight genes as mediators between the baseline International Prognostic Index score and overall survival.

genomics↗

bioRxiv2023

Albumin-binding recombinant human IL-18BP ameliorates macrophage activation syndrome and atopic dermatitis via direct IL-18 inactivation

Given the clinical success of cytokine blockade in managing diverse inflammatory human conditions, this approach could be exploited for numerous refractory or uncontrolled inflammatory conditions by identifying novel targets for functional blockade. IL-18, a pro-inflammatory cytokine, is relatively underestimated as a therapeutic target, despite accumulated evidence indicating the unique roles of IL-18 in acute and chronic inflammatory conditions, such as macrophage activation syndrome. Herein, we designed a new form of IL-18 blockade, i.e., APB-R3, a long-acting recombinant human IL-18BP linked to human albumin-binding Fab fragment, SL335, for extending half-life. We then explored the pharmacokinetics and pharmacodynamics of APB-R3. In addition to an extended serum half-life, APB-R3 alleviates liver inflammation and splenomegaly in a model of the macrophage activation syndrome induced in IL-18BP knockout mice. Moreover, APB-R3 substantially controlled skin inflammation in a model of atopic dermatitis. Thus, we report APB-R3 as a new potent IL-18 blocking agent that could be applied to treat IL-18-mediated inflammatory diseases.

immunology↗

bioRxiv2023

Speeding up interval estimation for R2-based mediation effect of high-dimensional mediators via cross-fitting

Mediation analysis is a useful tool in investigating how molecular phenotypes such as gene expression mediate the effect of exposure on health outcomes. However, commonly used mean-based total mediation effect measures may suffer from cancellation of component-wise mediation effects in opposite directions in the presence of high-dimensional omics mediators. To overcome this limitation, we recently proposed a variance-based R-squared total mediation effect measure that relies on the computationally intensive nonparametric bootstrap for confidence interval estimation. In the work described herein, we formulated a more efficient two-stage, cross-fitted estimation procedure for the R2 measure. To avoid potential bias, we performed iterative Sure Independence Screening (iSIS) in two subsamples to exclude the non-mediators, followed by ordinary least squares regressions for the variance estimation. We then constructed confidence intervals based on the newly derived closed-form asymptotic distribution of the R2 measure. Extensive simulation studies demonstrated that this proposed procedure is much more computationally efficient than the resampling-based method, with comparable coverage probability. Furthermore, when applied to the Framingham Heart Study, the proposed method replicated the established finding of gene expression mediating age-related variation in systolic blood pressure and identified the role of gene expression profiles in the relationship between sex and high-density lipoprotein cholesterol level. The proposed estimation procedure is implemented in R package CFR2M.

molecular biology↗

bioRxiv2021

Recovery of high-qualitied Genomes from a deep-inland Salt Lake Using BASALT

Metagenomic binning enables the in-depth characterization of microorganisms. To improve the resolution and efficiency of metagenomic binning, BASALT (Binning Across a Series of AssembLies Toolkit), a novel binning toolkit was present in this study, which recovers, compares and optimizes metagenomic assembled genomes (MAGs) across a series of assemblies from short-read, long-read or hybrid strategies. BASALT incorporates self-designed algorithms which automates the separation of redundant bins, elongate and refine best bins and improve contiguity. Evaluation using mock communities revealed that BASALT auto-binning obtained up to 51% more number of MAGs with up to 10 times better MAG quality from microbial community at low (132 genomes) and medium (596 genomes) complexity, compared to other binners such as DASTool, VAMB and metaWRAP. Using BASALT, a case-study analysis of a Salt Lake sediment microbial community from northwest arid region of China was performed, resulting in 426 non-redundant MAGs, including 352 and 69 bacterial and archaeal MAGs which could not be assigned to any known species from GTDB (ANI < 95%), respectively. In addition, two Lokiarchaeotal MAGs that belong to superphylum Asgardarchaeota were observed from Salt Lake sediment samples. This is the first time that candidate species from phylum Lokiarchaeota was found in the arid and deep-inland environment, filling the current knowledge gap of earth microbiome. Overall, BASALT is proven to be a robust toolkit for metagenomic binning, and more importantly, expand the Tree of Life.

bioinformatics↗

bioRxiv2019

State Change Probability: A Measure of the Complexity of Cardiac RR Interval Time Series Using Physiological State Change with Statistical Hypothesis Testing

The complexity of biological signals has been proposed to reflect the adaptability of a given biological system to different environments. Two measures of complexity--multiscale entropy (MSE) and entropy of entropy (EoE)--have been proposed, to evaluate the complexity of heart rate signals from different perspectives. The MSE evaluates the information content of a long time series across multiple temporal scales, while the EoE characterizes variation in amount of information, which is interpreted as the \"state changing,\" of segments in a time series. However, both are problematic when analyzing white noise and are sensitive to data size. Therefore, based on the concept of \"state changing,\" we propose state change probability (SCP) as a measure of complexity. SCP utilizes a statistical hypothesis test to determine the physiological state changes between two consecutive segments in heart rate signals. The SCP value is defined as the ratio of the number of state changes to total number of consecutive segment pairs. Two common statistical tests, the t-test and Wilcoxon rank-sum test, were separately used in the SCP algorithm for comparison, yielding similar results. The SCP method is capable of reasonably evaluating the complexity of white noise and other signals, including 1/f noise, periodic signals, and heart rate signals, from healthy subjects, as well as subjects with congestive heart failure or atrial fibrillation. The SCP method is also insensitive to data size. A universal SCP threshold value can be applied, to differentiate between healthy and pathological subjects for data sizes ranging from 100 to 10,000 points. The SCP algorithm is slightly better than the EoE method when differentiating between subjects, and is superior to the MSE method.

physiology↗

bioRxiv2018

Characterization of mannitol metabolism genes in Saccharina explains its key role in mannitol biosynthesis and evolutionary significance in Laminariales

As a unique photosynthetic product in brown algae, mannitol exhibits high synthesis and accumulation in Saccharina japonica. Mannitol acts as a carbon-storage compound and is an osmoprotectant, imparting increased tolerance to osmotic stress. However, the underlying biochemical and molecular mechanisms in macroalgae have not been studied. Analysis of genomic and transcriptomic data has shown that mannitol metabolism in S. japonica is a circular pathway composed of four steps. In this study, one S. japonica mannitol-1-phosphate dehydrogenase (M1PDH2) and two mannitol-1-phosphatase (M1Pase) proteins were recombinantly expressed to analysis enzyme biochemical properties. RNA sequencing and droplet digital polymerase chain reaction were used to analyze the gene expression patterns of mannitol metabolism in different generations, tissues, sexes, and abiotic stresses. Our findings revealed insights into the mannitol synthesis pathways in brown algae. All genes were constitutively expressed in all samples, allowing maintenance of basic mannitol anabolism and dynamic maintenance of the \"saccharide pool\" in vivo as the main storage and antistress mechanism. Enzyme assays confirmed that the recombinant proteins produced mannitol, with the specific activity of SjaM1Pase1 being 1.8-4831 times that of other algal enzymes. Combined with the transcriptional analysis, SjaM1Pase1 was shown to be the dominant gene of mannitol metabolism. Mannitol metabolism genes in multicellular filamentous (gametophyte) and large parenchyma thallus (sporophyte) generations had different expression levels and responded differently under environmental stresses (hyposaline and hyperthermia) in gametophytes and sporophytes. The considerable variation in enzyme characteristics and expression of mannitol synthesis genes suggest their important ecophysiological significance in the evolution of complex systems (filamentous and thallus) and environmental adaptation of Laminariales.

molecular biology↗

bioRxiv2018

The brown alga Saccharina japonica features distinct vanadium-dependent bromoperoxidases and iodoperoxidases

Marine algae have an extraordinary ability to absorb halogens which provide algae with an inorganic antioxidant impacting atmospheric chemistry. Although brown algal Laminariales species are the most efficient iodine accumulators among all living systems, and Saccharina japonica is the primary material used for iodine extraction, the functions and regulatory mechanisms of these species have not been fully documented. In this study, a functional genomics analysis of the algal vanadium-dependent haloperoxidase (vHPO) gene family was conducted; there genes can introduce halogen atoms into organic compounds. The comprehensive analyses regarding the bioinformatics and phylogenetics of novel genomic and transcriptomic sequencing data of 21 Rhodophyta and 19 Ochrophyta marine algal species revealed that brown algal vHPOs have two gene types, vanadium-dependent bromoperoxidase (vBPO) and vanadium-dependent iodoperoxidase (vIPO), with secondary endosymbiotic host origin. The enzyme activity of S. japonica vBPO and vIPO were verified for the first time and were quite stable in a wide range of temperature and pH values. However, the specific activity and optimal conditions were considerably different between vBPO and vIPO. The transcript expression analysis in different S. japonica tissues (including rhizoids), generations (sporophytes and gametophytes), sexes (male and female), and stress conditions (hyposaline and hyperthermia) also showed great differences between vBPOs and vIPOs. Most of the vBPOs were constitutively expressed with higher expression dose, which may be responsible for basal halogen metabolism. On the contrary, vIPOs mainly showed specific expression, which may be involved in tissue differentiation, generation differentiation, sex differentiation, and stress regulation. Comprehensive analysis of gene family evolution, enzyme biochemical characteristics, and complex transcriptional mechanisms were conducive to the environmental adaptation and sophisticated system evolution of Laminariales. The successful bromination of small-molecule compound substrate by SjavBPO provided high activity and efficient enzymatic tools for artificial synthesis of halogenated compounds.

molecular biology↗