Biology subjects

Ha, B.

Publications and source records attributed to Ha, B..

10 recordsLinked to original sources

bioRxiv2026

An Open Benchmark for Systems Vaccinology: Insights from the CMI-PB Challenges

Systems vaccinology approaches have identified factors affecting vaccine responses in multiple studies, but the ability of computational models to generalize these findings to unseen data remains unclear. We established a community resource to create and compare models predicting B. pertussis booster vaccination responses and put such modeling approaches to the test. We compiled multi-modal experimental training data from three independent cohorts (n=117 individuals), and asked investigators to predict vaccine responses in a cohort of 54 newly recruited individuals using only their pre-booster vaccination data. We benchmarked a total of 107 computational models. Top-performing models were characterized by workflows that prioritized rigorous data preprocessing, robust imputation of missing data, and the use of multi-omics integration or non-linear machine learning. We identified pre-existing antigen-specific antibody titers and baseline monocyte frequencies as the most consistent predictors of post-vaccination immunity, highlighting the dominant role of individual immune setpoints. We established the resulting datasets and evaluation framework as a community resource to advance predictive immunology and facilitate personalized vaccination strategies.

immunology↗

bioRxiv2026

H5N1 influenza binding and cell entry via human class II MHC, and blocking by cross-reactive antibodies

Summary paragraphHighly pathogenic avian influenza (HPAI) H5N1 clade 2.3.4.4b viruses are currently responsible for a multi-species outbreak affecting wild birds, poultry, numerous mammalian species, and humans. Influenza A viruses typically initiate infection through binding to sialic acid, although select bat and human influenza viruses can also exploit class II major histocompatibility complex (MHC-II) molecules for cell entry. Here we show that emerging H5N1 clade 2.3.4.4b viruses, but not historical H5 lineages, bind human MHC-II HLA-DR and mediate sialic acid-independent cell entry. Hemagglutinin binding to primary human immune cells varies with MHC-II expression and is further shaped by HLA-DR allelic variation, identifying host genetic determinants that may influence susceptibility to infection. Mammalian-adaptive substitutions within the hemagglutinin sialic acid receptor-binding domain reduce MHC-II binding, suggesting this interaction is remodeled during clade 2.3.4.4b H5 adaptation to a human host. Lastly, cross-reactive monoclonal antibodies isolated from clade 2.3.4.4b H5-naive humans can block the hemagglutinin-MHC-II interaction. These findings identify a previously unrecognized receptor pathway in contemporary H5N1 viruses and reveal that both human genetic variation and pre-existing humoral immunity can modulate this interaction, with implications for host range, cellular tropism, spillover risk, and therapeutic intervention.

immunology↗

bioRxiv2026

Immune Biomarkers of Islet Transplant Rejection Revealed by Synthetic Immunological Niche

Islet transplantation can restore glycemic control in type 1 diabetes, yet the heterogeneity of patient immune responses and transplant outcomes motivates the need for technologies to monitor the graft. Since transplanted islets are not readily accessible for biopsy due to their diffuse engraftment within the liver, clinical monitoring relies on measurements such as islet mass, blood glucose, and C-peptide levels, which are lagging indicators that change only after substantial graft injury. Here, we developed a minimally invasive synthetic immunological niche (IN) that captures graft-associated immune responses through serial subcutaneous biopsy. We evaluated the IN across murine syngeneic, allogeneic, and autoimmune islet transplant models, including CD40/CD154 costimulatory blockade with anti-CD40L. In syngeneic versus allogeneic recipients, IN identified immune populations and transcriptomic signatures that mirrored the graft and distinguished healthy from rejecting grafts. In anti-CD40L treated allografts, IN revealed innate macrophage- and dendritic cell-associated programs linked to graft acceptance versus rejection, whereas IN from untreated allografts showed stronger adaptive immune signatures. Longitudinal IN profiling further detected progressive inflammatory activation in accepted allografts, indicating persistent subclinical risk. Finally, in an autoimmune allograft model treated with anti-CD40L plus rapamycin, IN identified a 13-gene signature that separated early from late rejection trajectories and distinguished autoimmune-from alloimmune-associated rejection programs. Overall, these findings establish IN as a surrogate tissue for minimally invasive monitoring of islet graft and early detection of rejection-associated immune dysregulation. One Sentence SummaryAn engineered immunological niche captures distinct immune signatures of allo- and auto-mediated islet transplant rejection

bioengineering↗

bioRxiv2026

Synthetic Immunological Niche Reveals Early Immune Dysregulationand Stratifies Therapeutic Response in Type 1 Diabetes

In Type 1 Diabetes (T1D), disease onset and response to immunotherapy vary widely among individuals, reflecting heterogeneous stage-specific immune dysregulation that remains undefined. To investigate this heterogeneity, we engineered a microporous polycaprolactone scaffold that forms a synthetic immunological niche (IN) upon subcutaneous implantation, enabling in vivo capture of systemic immune dysregulation. In non-obese diabetic (NOD) mice, longitudinal transcriptomic profiling of IN-infiltrating cells identified early-stage genes relatively enriched for myeloid cells, followed by progressive increases in T cell-associated dysregulation at later stages that distinguished T1D progressors from non-progressors. We derived an early-stage IN-based T1D gene signature capturing immune alterations. The signature stratified NOD progressors from non-progressors as early as 6 weeks of age and was conserved across human T1D datasets, distinguishing T1D from non-diabetic individuals in spleen and pancreatic lymph node samples, but not peripheral blood. Signature-based stratification further revealed enrichment of macrophage-associated TNF- pathways in NOD progressors, validated in human T1D islets. Given heterogeneous response to anti-TNF- therapy, IN profiling identified resistance-associated mechanisms and enabled derivation of a pathway score that prospectively distinguished treatment-sensitive from resistant mice prior to therapy, establishing the IN as a minimally invasive platform for detecting stage-wise immune dysregulation and stratifying immunotherapy response in T1D.

bioengineering↗

bioRxiv2025

T Cells Tear Apart Confining Extracellular Matrix Via a Breaststroke-like Motion to Generate Migration Paths

T cells adeptly migrate through soft tissues to target aberrant cells and regulate immunity. However, how they establish migration paths in confining nanoporous extracellular matrices (ECMs), and why they often fail to do so in dense ECMs that occur during fibrosis and around tumors, remain unclear. Here, we studied T cell migration in confining collagen-rich hydrogels spanning a range of stiffness, viscoelasticity, mechanical plasticity, and shear strength. Strikingly, only shear strength--the stress required for material failure--correlated strongly with migration, challenging the long-held focus on stiffness and pore size in cell motility. During migration, T cells extend actin-rich, finger-like protrusions into the ECM, which then undergo divergent breaststroke-like motion. Thus, T cells tear apart confining matrices using breaststroke-like motion to generate migration paths.

biophysics↗

bioRxiv2024

Putting computational models of immunity to the test - an invited challenge to predict B. pertussis vaccination outcomes

Systems vaccinology studies have been used to build computational models that predict individual vaccine responses and identify the factors contributing to differences in outcome. Comparing such models is challenging due to variability in study designs. To address this, we established a community resource to compare models predicting B. pertussis booster responses and generate experimental data for the explicit purpose of model evaluation. We here describe our second computational prediction challenge using this resource, where we benchmarked 49 algorithms from 53 scientists. We found that the most successful models stood out in their handling of nonlinearities, reducing large feature sets to representative subsets, and advanced data preprocessing. In contrast, we found that models adopted from literature that were developed to predict vaccine antibody responses in other settings performed poorly, reinforcing the need for purpose-built models. Overall, this demonstrates the value of purpose-generated datasets for rigorous and open model evaluations to identify features that improve the reliability and applicability of computational models in vaccine response prediction.

immunology↗

bioRxiv2023

A systems vaccinology resource to develop and test computational models of immunity

Computational models that predict an individuals response to a vaccine offer the potential for mechanistic insights and personalized vaccination strategies. These models are increasingly derived from systems vaccinology studies that generate immune profiles from human cohorts pre- and post-vaccination. Most of these studies involve relatively small cohorts and profile the response to a single vaccine. The ability to assess the performance of the resulting models would be improved by comparing their performance on independent datasets, as has been done with great success in other areas of biology such as protein structure predictions. To transfer this approach to system vaccinology studies, we established a prototype platform that focuses on the evaluation of Computational Models of Immunity to Pertussis Booster vaccinations (CMI-PB). A community resource, CMI-PB generates experimental data for the explicit purpose of model evaluation, which is performed through a series of annual data releases and associated contests. We here report on our experience with the first such dry run for a contest where the goal was to predict individual immune responses based on pre-vaccination multi-omic profiles. Over 30 models adopted from the literature were tested, but only one was predictive, and was based on age alone. The performance of new models built using CMI-PB training data was much better, but varied significantly based on the choice of pre-vaccination features used and the model building strategy. This suggests that previously published models developed for other vaccines do not generalize well to Pertussis Booster vaccination. Overall, these results reinforced the need for comparative analysis across models and datasets that CMI-PB aims to achieve. We are seeking wider community engagement for our first public prediction contest, which will open in early 2024.

systems biology↗

bioRxiv2023

Monocytes use protrusive forces to generate migration paths in viscoelastic collagen-based extracellular matrices

Circulating monocytes are recruited to the tumor microenvironment, where they can differentiate into macrophages that mediate tumor progression. To reach the tumor microenvironment, monocytes must first extravasate and migrate through the type-1 collagen rich stromal matrix. The viscoelastic stromal matrix around tumors not only stiffens relative to normal stromal matrix, but often exhibits enhanced viscous characteristics, as indicated by a higher loss tangent or faster stress relaxation rate. Here, we studied how changes in matrix stiffness and viscoelasticity, impact the three-dimensional migration of monocytes through stromal-like matrices. Interpenetrating networks of type-1 collagen and alginate, which enable independent tunability of stiffness and stress relaxation over physiologically relevant ranges, were used as confining matrices for three-dimensional culture of monocytes. Increased stiffness and faster stress relaxation independently enhanced the 3D migration of monocytes. Migrating monocytes have an ellipsoidal or rounded wedge-like morphology, reminiscent of amoeboid migration, with accumulation of actin at the trailing edge. Matrix adhesions were dispensable for monocyte migration in 3D, but migration did require actin polymerization and myosin contractility. Mechanistic studies indicate that actin polymerization at the leading edge generates protrusive forces that open a path for the monocytes to migrate through in the confining viscoelastic matrices. Taken together, our findings implicate matrix stiffness and stress relaxation as key mediators of monocyte migration and reveal how monocytes use pushing forces at the leading edge mediated by actin polymerization to generate migration paths in confining viscoelastic matrices. Significance StatementCell migration is essential for numerous biological processes in health and disease, including for immune cell trafficking. Monocyte immune cells migrate through extracellular matrix to the tumor microenvironment where they can play a role in regulating cancer progression. Increased extracellular matrix (ECM) stiffness and viscoelasticity have been implicated in cancer progression, but the impact of these changes in the ECM on monocyte migration remains unknown. Here, we find that increased ECM stiffness and viscoelasticity promote monocyte migration. Interestingly, we reveal a previously undescribed adhesion-independent mode of migration whereby monocytes generate a path to migrate through pushing forces at the leading edge. These findings help elucidate how changes in the tumor microenvironment impact monocyte trafficking and thereby disease progression.

cell biology↗

bioRxiv2019

CellGPS: Whole-body tracking of single cells by positron emission tomography

In vivo molecular imaging tools are critically important for determining the role played by cell trafficking in biological processes and cellular therapies. However, existing tools measure average cell behavior and not the kinetics and migration routes of individual cells inside the body. Furthermore, efflux and non-specific accumulation of contrast agents are confounding factors, leading to inaccurate estimation of cell distribution in vivo. In view of these challenges, we report the development of a \"cellular GPS\" capable of tracking single cells inside living subjects with exquisite sensitivity. We use mesoporous silica nanoparticles (MSN) to concentrate 68Ga radioisotope into live cells and inject these cells into live mice. From the pattern of annihilation photons detected by positron emission tomography (PET), we infer, in real time, the position of individual cells with respect to anatomical landmarks derived from X-ray computed tomography (CT). To demonstrate this technique, a single human breast cancer cell was tracked in a mouse model of experimental metastasis. The cell arrested in the lungs 2-3 seconds after tail-vein injection. Its average velocity was estimated at around 50 mm/s, consistent with blood flow rate. Other cells were tracked after injection through other routes, but no motion was detected within 10 min of acquisition. Single-cell tracking could be applied to determine the kinetics of cell trafficking and arrest during the earliest phase of the metastatic cascade, the trafficking of immune cells during cancer immunotherapy, or the distribution of cells after transplantation in regenerative medicine.

bioengineering↗

bioRxiv2019

Uncovering the cell type specificity of blood sample derived gene signatures using RNA expression data

Analysis of transcriptomic data derived from blood samples is complicated by the complex mixture of cell types such samples contain. Transcriptomic signatures derived from such samples are often driven by a particular cell lineage within the mixture. Identifying this most contributing lineage can help to provide a biological interpretation of the signature. We created a web application CellTypeScore which quantifies and visually represents the expression level of signature genes in common blood cell types. This is done by constructing an interactive stacked bar plot with the bars representing expression of genes across blood cell types. Summed scores serve as a measure of how highly the combined signature is expressed in different cell types. An online version of the application can be found at https://tools.dice-database.org/celltypescore/.

bioinformatics↗