Biology subjects

Wang, Z. J.

Publications and source records attributed to Wang, Z. J..

17 recordsLinked to original sources

bioRxiv2026

A Generative Virtual Tissue Model Enables Computational Design of Therapeutic Perturbation Strategies

Computational design has transformed many fields of engineering, where simulators can explore millions of candidate design configurations before experimental development and testing. Therapeutic design in biomedicine has resisted computational design approaches because disease progression and therapeutic response emerge from interactions among many cell types within human tissue, governed by biochemical parameters that are largely unknown and potentially unknowable. Here, we introduce the Cell Interaction Foundation Model (CIFM), a virtual tissue model that forward-simulates the transcriptional dynamics of cells in human tissue under arbitrary therapeutic conditions based upon a spatial transcriptomic seed. CIFM is a geometric graph neural network trained by self-supervised masked-transcriptome prediction on millions of cellular microenvironments spanning human tissue types and disease states; generative, auto-regressive, monte-carlo play-out, then, simulates transcriptional dynamics under combinatorial perturbations from a spatial transcriptomic seed. We validate CIFM by showing accuracy gains in gene expression prediction and imputation, disease classification, recapitulation of perturbation responses in prostate cancer models, and recovery of T cell-tumor signaling measured in cell-cell sequencing experiments. Beyond such conventional tasks, CIFM enables target identification and therapeutic design through generative tissue simulation play-outs. Analyzing over 106 single and combinatorial perturbations, CIFM designs immunotherapy strategies for cancer and autoimmune disease that exploit combinatorial manipulation of signaling pathways to induce or suppress immune activation. Broadly, CIFM shows how generative artificial intelligence methods can be applied to model emergent behavior in highly interacting biological systems, yielding new approaches to fundamental understanding of tissue behavior as well as large-scale therapeutic design.

bioinformatics↗

bioRxiv2025

Global Mapping of Combinatorial Chromatin Regulatory Events Using Hi-Plex CUT&Tag

Epigenetic modulators, transcription factors, and chromatin-associated proteins collaboratively regulate essential genomic functions, such as transcription, repression, and DNA damage repair. However, the intricate cross-talk or the interaction between these chromatin regulatory factors (CRFs) remains poorly understood, largely due to limitations in experimental methods, which traditionally interrogate the genomic locations of only one CRF. Although the latest methods can study multiple CRFs simultaneously, they cannot examine the interaction between these factors. Inferring biological interactions often requires integrating data from multiple experiments, which can be labor-intensive and imprecise since the measurement is not from the same DNA molecule. To address these challenges, we developed Hi-Plex CUT&Tag, an advanced technology that enables the robust detection of co-localization events across over 600 pairs of CRFs using 36 barcoded monoclonal antibodies (mAbs), while minimizing background signals and cross-contamination. Hi-Plex CUT&Tag facilitates comprehensive pairwise analysis of epigenetic modifiers, including histone post-translational modifications (PTMs), writers, and transcription factors (TFs). Each Tn5 tagmentation fragment provides detailed insights, capturing the identities of two co-localized events, the underlying genomic sequences, and the molecular distance between them. For the first time, numerous novel bivalent events, epigenetic-context-dependent transcriptional regulation, and specific chromatin mark combinations with significant impacts on gene regulation can be detected in a single experiment. Furthermore, single-cell Hi-Plex CUT&Tag enables the analysis of synergistic interactions between CRF pairs at the single-cell level, providing unprecedented resolution for studying chromatin dynamics.

genetics↗

bioRxiv2025

In Silico Optimisation of Regenerative Cell Therapy in the Infarcted Human Ventricles to Mitigate Arrhythmic Burden

Myocardial infarction remains a frequent cause of heart failure and mortality. Cell therapy has been shown promising in pre-clinical trials to regenerate the damaged tissue, but delivered cells may beat spontaneously and produce arrhythmias in the ventricles, particularly in the first weeks after delivery, which hinders clinical application. Previous studies have proposed ionic targets to supress the cells automaticity but, so far, the effects of such treatments on the cells calcium dynamics, as a key driver of contractile function, have been insufficiently evaluated. Furthermore, effective strategies are needed that can alleviate the injected cells pro-arrhythmic action potential phenotypes. The goal of our study was to identify mechanisms to mitigate arrhythmic pathways following cell delivery in the chronically infarcted human ventricles using multiscale modelling and simulation. First, we demonstrate credibility by simulating experimentally observed transient automaticity-induced ventricular tachycardia arrhythmias with a frequency of up to 140 beats per minute at two weeks post cell injection in three different infarct geometries. Next, our simulations show how the timeframe during which re-entry was inducible increases 1) from before to after cell delivery at day 0 from 0 to 640 ms, 60 to 100 ms, and 60 to 760 ms in the small, medium, and large scar, respectively, and 2) from day 0 to day 14 after virtual cell injection in the large scar by 175%. Finally, we show that a combination of blocking the funny current and upregulating the inward rectifier potassium current, the sodium potassium pump, and the rapid delayed outward rectifier potassium current can reduce both automaticity-induced and re-entrant arrhythmias while maximising calcium amplitudes. In conclusion, our simulations show that not only automaticity-induced but also re-entrant arrhythmias increase as injected cells mature in the ventricles and depend on the scar size. Furthermore, through modelling and simulation, we identify anti-arrhythmic strategies to improve therapy safety while maximising efficacy.

bioengineering↗

bioRxiv2025

Sequential Amino Acid Mutagenesis-Driven De Novo Evolution of Adenine Deaminases Enables Efficient In Vivo Base Editing in Primate

Base editing allows for the precise modification of genetic information, providing new avenues for treating diseases1. The adenine base editor ABE8e is currently the most efficient and widely used tool for adenine base editing. ABE8e was developed through multiple rounds of directed evolution of Escherichia coli tRNA adenine deaminase, including phage-assisted continuous evolution (PACE)2,3. While PACE is highly effective, it is a complex system that poses challenges for implementation4. Despite its high efficiency, ABE8e is associated with limitations such as relatively higher bystander editing effects and elevated off-target activity5, which need to be addressed to further enhance its precision and safety. Here, we developed a novel method for the de novo discovery of evolved ABE components, particularly adenine deaminases. This process involves identifying candidate proteins through AI-based structural prediction and clustering, followed by the enhancement of deaminase editing activity through screening libraries created by sequential amino acid saturation mutagenesis. This evolutionary strategy simplifies the approach by employing saturation mutagenesis libraries tailored to specific segments, thereby enabling exploration of an expanded sequence space and increasing the likelihood of discovering adenine deaminases with superior capabilities. The newly developed hpABE5.20 here demonstrates a more refined editing window, reduced DNA off-target effects that are both sgRNA-dependent and -independent, and minimized RNA off-target activity, while maintaining robust editing efficiency relative to ABE8e. Furthermore, hpABE5.20 has been successfully applied for precise and effective therapeutic adenine base editing in cellular disease models, humanized mice, and non-human primates.

bioengineering↗

bioRxiv2025

MonoAlg3D: Enabling Cardiac Electrophysiology Digital Twins with an Efficient Open Source Scalable Solver on GPU Clusters

Modelling and simulation are essential in biomedicine, and specifically in computational cardiology. Reliable, efficient and accurate solvers are critical. This study presents an open source, GPU-based cardiac electrophysiology solver for scalable digital twin multiscale simulations (MO_SCPLOWONOC_SCPLOWAO_SCPLOWLGC_SCPLOW3D), incorporating conduction system calibration and performance optimization. The solver employs the monodomain equation coupled with the Purkinje network, solved via the finite volume method, featuring a GPU-based linear solver and concurrent simulation dispatch with MPI. We demonstrate a 10.94x speedup over a CPU-based solution and scalability by running 512 simulations on 128 compute nodes, completing all coarse-mesh simulations in less than 24 minutes and fine-mesh simulations in 303 minutes. We also demonstrate integration into a cardiac digital twin pipeline for personalisation based on clinical data. The proposed open source solver enhances computational efficiency and physiological fidelity, enabling large-scale, high-speed cardiac simulations. This work marks a significant step toward fast and scalable cardiac simulations on GPU architectures, with integration in a Digital Twin personalisation pipeline including the conduction system.

bioengineering↗

bioRxiv2025

Sex-specific human electromechanical multiscale in-silico models for virtual therapy evaluation

Background and AimWomen are significantly under-represented in cardiovascular research and in the evaluation of treatment safety and efficacy, leading to poorer patient outcomes. Quantification and investigation of sex differences in human electromechanical function and underlying mechanisms is crucial. To address this, we present sex-specific human cellular and biventricular electromechanical models for mechanistic investigations into sex-differences in therapy evaluation through simulations. MethodsProtein genomic expression data from healthy human myocytes were used to calibrate sex-specific models of human cellular electrophysiology, subsequently integrated in biventricular electromechanical models with male and female anatomies. A validation, verification and uncertainty evaluation were implemented at the cellular and biventricular level, including validation using sex-specific datasets from randomised controlled trials for Dofetilide and Verapamil, with known sex-differences. Ionic mechanisms underlying sex-differences in drug response were mechanistically investigated. ResultsSex-specific electromechanical models recapitulate sex-differences from ionic currents to ECG biomarkers including QTc interval (Male: 312ms; Female: 339ms; 9% difference), T-wave amplitude (6-9% difference) and ST-steepness through electrophysiological changes alone. Sex-specific simulations demonstrate both ECG biomarkers and mechanical biomarkers (Female LVEF: 68%, Male LVEF: 50%) within healthy ranges in clinical data for male and female population in the UK Biobank (n= 806, 46% Male). ECGs sex-differences are primarily explained by ionic currents, whereas mechanical sex-differences are driven by anatomical differences, and secondarily through more robust calcium function in females. Under Dofetilide, simulations show exacerbated QT prolongation in women compared to men (54-78% increase in effect), and a T-wave amplitude decrease in males (up to 0.25 mV), consistent with clinical data. This is explained in simulations by lower repolarisation reserve in women (due to low potassium and high calcium currents) than men. Verapamil shows no effect on simulated QT in either sex, and divergent T-wave modulation (increased amplitude in females, decreased in males) consistent with clinical trial data. Simulations identify enhanced contractile reservoir in female compared to male, with lesser decreases to ejection fraction with calcium current block. ConclusionSimulations using novel, sex-specific cellular and biventricular electromechanical models reveal the primary role of ionic currents sex-differences in ECG and drug response, whereas mechanical sex-differences are also underpinned by anatomical differences. Main ContributionsO_LIDevelopment, calibration and validation of sex-specific human ventricular electromechanical, multiscale models. C_LIO_LIAn analysis of clinical randomised trial data in the context of sex-specific effects of multi-channel blockers on the ECG by dosage, where previous analysis focused on pharmacokinetics. C_LIO_LIConsideration of both sex-specific electrophysiology and anatomy explains sex-specific differences on the impact of drugs on ECG and mechanics. C_LIO_LISimulations demonstrate that the reduced repolarisation reserve in females increases the susceptibility to QTc prolongation via potassium channel block compared to males, and more robust calcium dynamics protect against t-wave amplitude reduction and the more severe contractility loss through L-type calcium inhibition observed in males. C_LI

physiology↗

bioRxiv2025

Calibration and validation strategy for electromechanical cardiac digital twins

State-of-the-art cardiac electromechanical modelling and simulation form the basis for recent developments in cardiac Digital Twin technologies. However, a comprehensive evaluation of electromechanical models at cellular, tissue, and organ level has yet to be performed that addresses both ECG and pressure-volume biomarkers. Such an evaluation would build credibility for applications of cardiac Digital Twins in clinical research and therapy development. We aimed to follow ASME V&V40 standards for calibration, validation, and sensitivity analysis of ventricular electromechanical models under healthy conditions, to progress towards the Digital Twin vision. We performed a multi-scaled review of ventricular electromechanics and compiled a dataset for calibration and validation incorporating ECG, pressure-volume, displacement, and strain biomarkers. When applied to a biventricular multiscale model, we achieved healthy calibrated values for the QRS duration (89 ms), QT interval (360 ms), left ventricular ejection fraction (LVEF) (51 %), peak systolic pressure (14 kPa), end diastolic (105 mL) and end systolic volumes (51 mL), peak ejection flow rate (180 mL/ms). Model validation was performed by comparison to displacement and strain biomarkers including systolic atrioventricular plane displacement (1.5 cm), systolic fibre strain (-0.18) and longitudinal strain (-0.15). Sensitivity analysis of model parameters at cellular and ventricular scales was also performed to inform model calibration and as a first step towards uncertainty quantification. We quantified the effects of variability in ionic conductance, mechanical stiffness, cross-bridge cycling dynamics, and systemic circulation on action potential and active tension dynamics at the cellular scale, and on ECG, pressure-volume, displacement, and strain biomarkers at the ventricular scale. Simulations showed that the relationship between healthy LVEF and T wave biomarkers was primarily underpinned by variability in L-type calcium channel conductance and SERCA activity through multi-scale effects. In this study, we provide a systematic framework for credibility assessment of cardiac electromechanical models based on both ECG and mechanical biomarkers, as a foundational step towards the cardiac Digital Twin vision.

bioengineering↗

bioRxiv2024

In vivo genome editing of human hematopoietic stem cells for treatment of blood disorders by mRNA delivery

Ex vivo autologous hematopoietic stem cells (HSCs) gene therapy provides promising new treatments for hematological disorders. However, current methods involve complex processes and chemotherapeutic conditioning, leading to limited accessibility for treatment and significant side effects. Here, we developed an antibody-free targeted lipid nanoparticles (LNPs) for mRNA delivery to HSCs in vivo, enabling efficient base editing of the HBG target in human HSCs to reactivate fetal hemoglobin in derived erythroid cells. Delivery of ABE8e/sgRNA mRNA with optimized structure LNPs achieves efficient in vivo base editing of HBG in transfusion-dependent {beta}-thalassemia (TDT) patients derived HSCs engrafted in immunodeficient NCG-X mice, showing restored globin chain balance in erythroid cells. Our research indicated that utilizing LNPs for delivery of genome editor achieves efficient editing of endogenous genes of human HSCs. Notably, this non-viral delivery system eliminates the need for harvesting or mobilizing HSCs, providing a potent and one-time treatment potential for blood disorders like sickle cell disease (SCD) and TDT. HighlightsO_LIAntibody-free targeted LNPs system achieves efficient mRNA delivery and base editing in human bone marrow cells including HSCs via one-time intravenous injection. C_LIO_LIDelivery of ABE8e/sgRNA mRNA with optimized LNPs achieves efficient in vivo base editing of HBG in TDT patients derived HSCs engrafted in immunodeficient NCG-X mice, showing restored globin chain balance in erythroid cells. C_LIO_LILNPs efficiently deliver gene editing system to the BM for in vivo editing of human HSCs, providing preclinical evidence for the next generation non-viral gene therapy for blood disorders. C_LI

bioengineering↗

bioRxiv2024

A Supervised Contrastive Framework for Learning Disentangled Representations of Cell Perturbation Data

CRISPR technology, combined with single-cell RNA-Seq, has opened the way to large scale pooled perturbation screens, allowing more systematic interrogations of gene functions in cells at scale. However, such Perturb-seq data poses many analysis challenges, due to its high-dimensionality, high level of technical noise, and variable Cas9 efficiency. The single-cell nature of the data also poses its own challenges, as we observe the heterogeneity of phenotypes in the unperturbed cells, along with the effect of the perturbations. All in all, these characteristics make it difficult to discern subtler effects. Existing tools, like mixscape and ContrastiveVI, provide partial solutions, but may oversimplify biological dynamics, or have low power to characterize perturbations with a smaller effect size. Here, we address these limitations by introducing the Supervised Contrastive Variational Autoencoder (SC-VAE). SC-VAE integrates guide RNA identity with gene expression data, ensuring a more discriminative analysis, and adopts the Hilbert-Schmidt Independence Criterion as a way to achieve disentangled representations, separating the heterogeneity in the control population from the effect of the perturbations. Evaluation on large-scale data sets highlights SC-VAEs superior sensitivity in identifying perturbation effects compared to ContrastiveVI, scVI and PCA. The perturbation embeddings better reflect known protein complexes (evaluated on CORUM), while its classifier offers promise in identifying assignment errors and cells escaping the perturbation phenotype. SC-VAE is readily applicable across diverse perturbation data sets.

bioinformatics↗

bioRxiv2023

In Silico Evaluation of Cell Therapy in Acute versus Chronic Infarction: Role of Automaticity, Heterogeneity and Purkinje in Human

Human-based modelling and simulation offer an ideal testbed for novel medical therapies to guide experimental and clinical studies. Myocardial infarction (MI) is a common cause of heart failure and mortality, for which novel therapies are urgently needed. Although cell therapy offers promise, electrophysiological heterogeneity raises pro-arrhythmic safety concerns, where underlying complex spatio-temporal dynamics cannot be investigated experimentally. After demonstrating credibility of the modelling and simulation framework, we investigate cell therapy in acute versus chronic MI, and the role of cell heterogeneity, scar size and the Purkinje system. Simulations agreed with experimental and clinical recordings from ionic to ECG dynamics in acute and chronic infarction. Following cell delivery, spontaneous beats were facilitated by heterogeneity in cell populations, chronic MI due to tissue depolarisation, and slow sinus rhythm. Subsequent re-entrant arrhythmias occurred, in some instances with Purkinje involvement, and their susceptibility was enhanced by impaired Purkinje-myocardium coupling, large scars, and acute infarction. We conclude that homogeneity in injected cell populations minimises their spontaneous beating, which is enhanced by chronic MI, whereas a healthy Purkinje-myocardium coupling is key to prevent subsequent re-entrant arrhythmias, particularly for large scars.

bioengineering↗

bioRxiv2023

Multi-ContrastiveVAE disentangles perturbation effects in single cell images from optical pooled screens

Optical pooled screens (OPS) enable comprehensive and cost-effective interrogation of gene function by measuring microscopy images of millions of cells across thousands of perturbations. However, the analysis of OPS data still mainly relies on hand-crafted features, even though these are difficult to deploy across complex data sets. This is because most unsupervised feature extraction methods based on neural networks (such as auto-encoders) have difficulty isolating the effect of perturbations from the natural variations across cells and experimental batches. Here, we propose a contrastive analysis framework that can more effectively disentangle the phenotypes caused by perturbation from natural cell-cell heterogeneity present in an unperturbed cell population. We demonstrate this approach by analyzing a large data set of over 30 million cells imaged across more than 5, 000 genetic perturbations, showing that our method significantly outperforms traditional approaches in generating biologically-informative embeddings and mitigating technical artifacts. Furthermore, the interpretable part of our model distinguishes perturbations that generate novel phenotypes from the ones that only shift the distribution of existing phenotypes. Our approach can be readily applied to other small-molecule and genetic perturbation data sets with highly multiplexed images, enhancing the efficiency and precision in identifying and interpreting perturbation-specific phenotypic patterns, paving the way for deeper insights and discoveries in OPS analysis.

bioinformatics↗

bioRxiv2023

Generating counterfactual explanations of tumor spatial proteomes to discover effective strategies for enhancing immune infiltration

Cancer progression can be slowed down or halted via the activation of either endogenous or engineered T cells and their infiltration of the tumour microenvironment. Here we describe a deep-learning model that uses large-scale spatial proteomic profiles of tumours to generate minimal tumour perturbations that boost T-cell infiltration. The model integrates a counterfactual optimization strategy for the generation of the perturbations with the prediction of T-cell infiltration as a self-supervised machine-learning problem. We applied the model to 368 samples of metastatic melanoma and colorectal cancer assayed using 40-plex imaging mass cytometry, and discovered cohort-dependent combinatorial perturbations (CXCL9, CXCL10, CCL22 and CCL18 for melanoma, and CXCR4, PD-1, PD-L1 and CYR61 for colorectal cancer) that support T-cell infiltration across patient cohorts, as confirmed via in vitro experiments. Leveraging counterfactual-based predictions of spatial omics data may aid the design of cancer therapeutics.

cancer biology↗

bioRxiv2022

Human iPSC-CMs and in-silico technologies define mechanisms and accelerate targeted pharmacogenetics in hypertrophic cardiomyopathy

Cardiomyopathies have unresolved genotype-phenotype relationships and lack disease-specific treatments. Here we identify genotype-specific pathomechanisms and therapeutic targets combining experimental hiPSC-CM modelling and human-based cardiac electromechanical in-silico modelling and simulation bridging from specific mutations to clinical biomarkers. We select hypertrophic cardiomyopathy as a challenge for this approach and study genetic variations that mutate proteins of the thick (MYH7R403Q/+) and thin filaments (TNNT2R92Q/+, TNNI3R21C/+) of the cardiac sarcomere. We show that destabilisation of myosin super relaxation drives disease in MYH7R403Q/+ with secondary effects on thin filament activation, which are corrected by Mavacamten. Thin filament variants TNNT2R92Q/+ and TNNI3R21C/+ share calcium regulation-related pathomechanisms, for which Mavacamten provides incomplete salvage. We define the ideal characteristics of a novel thin filament-targeting compound and show its efficacy in-silico. We demonstrate that hybrid human-based hiPSC-CM and in-silico studies accelerate pathomechanism discovery and classification testing, improving clinical interpretation of genetic variants, and directing rational therapeutic targeting and design.

bioengineering↗

bioRxiv2022

Ionic remodelling following myocardial infarction explains phenotypic variability in ECG and arrhythmic substrate but not in ejection fraction

AimsSudden death after myocardial infarction (MI) is associated with electrophysiological heterogeneities and ionic remodelling, which are reflected as variable phenotypes. Low ejection fraction (EF) is used in risk stratification, but its mechanistic links with the post-MI pro-arrhythmic heterogeneities are unknown. We aim to provide a mechanistic explanation of clinical phenotypes in acute and chronic MI, from ionic remodeling to ECG and EF, using human electromechanical modelling and simulation to augment experimental and clinical investigations. Methods and ResultsA human ventricular electromechanical modelling and simulation framework is constructed and validated with rich experimental and clinical datasets. Abnormalities caused by scar and border zone ionic remodeling are introduced in varying degrees as reported in experimental data obtained in acute and chronic infarction. Simulations enabled reproducing and explaining clinical phenotypes post-MI, from ionic remodelling to ECGs and pressure-volume loops. In acute MI, T-wave inversion and Brugada phenocopy were explained by up to 57 ms of local APD prolongation and activation failure due to the inhibition of potassium, sodium and calcium channels in the border zone. In chronic MI, upright tall T-waves highlight large repolarisation dispersion caused by uneven potassium channel expression in border and remote zones, which promoted ectopic propagation at fast pacing. Post-MI ionic remodelling reduced EF by up to 10% through inhibition of calcium transient amplitude due to weaker calcium currents or SERCA activity, but the EF at resting heart rate was not sensitive to the extent of repolarisation heterogeneity and the risk of repolarisation abnormalities at fast pacing. ConclusionsMulti-scale modelling and simulation coherently integrates experimental and clinical data at subcellular, tissue, and organ scales to unravel electromechanical disease mechanisms in MI. In acute post-MI, ionic remodelling and its effect on refractoriness and propagation failure in the BZ have a strong impact on phenotypic ECG variability, whereas in chronic post-MI, the repolarisation dispersion across the BZ is crucial. T-wave and QT abnormalities are better indicators of repolarisation heterogeneities than EF in post-MI.

bioengineering↗

bioRxiv2021

Signaling receptor localization maximizes cellular information acquisition in spatially-structured, natural environments

Cells in natural environments like tissue or soil sense and respond to extracellular ligands with intricately structured and non-monotonic spatial distributions, sculpted by processes such as fluid flow and substrate adhesion. In this work, we show that spatial sensing and navigation can be optimized by adapting the spatial organization of signaling pathways to the spatial structure of the environment. We develop an information-theoretic framework for computing the optimal spatial organization of a sensing system for a given signaling environment. We find that receptor localization maximizes information acquisition in simulated natural contexts, including tissue and soil. Receptor localization extends naturally to produce a dynamic protocol for continuously redistributing signaling receptors, which when implemented using simple feedback, boosts cell navigation efficiency by 30-fold. Broadly, our work shows how cells can maximize the fidelity of information transfer by adapting the spatial organization of signaling molecules to the spatial structure of the environment.

systems biology↗

bioRxiv2019

Classifying T cell activity in autofluorescence intensity images with convolutional neural networks

The importance of T cells in immunotherapy has motivated developing technologies to better characterize T cells and improve therapeutic efficacy. One specific objective is assessing antigen-induced T cell activation because only functionally active T cells are capable of killing the desired targets. Autofluorescence imaging can distinguish T cell activity states of individual cells in a non-destructive manner by detecting endogenous changes in metabolic co-enzymes such as NAD(P)H. However, recognizing robust patterns of T cell activity is computationally challenging in the absence of exogenous labels or information-rich autofluorescence lifetime measurements. We demonstrate that advanced machine learning can accurately classify T cell activity from NAD(P)H intensity images and that those image-based signatures transfer across human donors. Using a dataset of 8,260 cropped single-cell images from six donors, we meticulously evaluate multiple machine learning models. These range from traditional models that represent images using summary statistics or extract image features with CellProfiler to deep convolutional neural networks (CNNs) pre-trained on general non-biological images. Adapting pre-trained CNNs for the T cell activity classification task provides substantially better performance than traditional models or a simple CNN trained with the autofluorescence images alone. Visualizing the images with dimension reduction provides intuition into why the CNNs achieve higher accuracy than other approaches. However, we observe that fine-tuning all layers of the pre-trained CNN does not provide a classification performance boost commensurate with the additional computational cost. Our software detailing our image processing and model training pipeline is available as Jupyter notebooks at https://github.com/gitter-lab/t-cell-classification.

bioinformatics↗

bioRxiv2019

A novel reporter gene assay for pyrogen detection

Fever is a systemic inflammatory response of the body to pyrogens. Nuclear factor {kappa}B (NF-{kappa}B) is a central signalling molecule that causes the excessive secretion of various proinflammatory factors induced by pyrogens. This study explored the feasibility of a novel reporter gene assay (RGA) for pyrogen detection using RAW 264.7 cells stably transfected with the NF-{kappa}B reporter gene as a pyrogenic marker. Pyrogen was incubated with the transgenic cells, and the intensity of the fluorescence signal generated by luciferase secreted by the reporter gene was used to reflect the degree of activation of NF-{kappa}B, so as to quantitatively detect the pyrogens. The RGA could detect different types of pyrogens, including the lipopolysaccharide (LPS) of gram-negative bacteria, the lipoteichoic acid (LTA) of gram-positive bacteria, and the zymosan of fungi, and a good dose-effect relationship was observed in terms of NF-{kappa}B activity. The limits of detection of the RGA to those pyrogens were 0.03 EU/ml, 0.001 g/ml, and 1 g/ml, respectively. The method had good precision and accuracy and could be applied to many biological products (e.g., nivolumab, rituximab, bevacizumab, etanercept, basiliximab, haemophilus influenzae type b conjugate vaccine, 23-valent pneumococcal polysaccharide vaccine, and group A and group C meningococcal conjugate vaccine). The results of this study suggest that the novel RGA has a wide pyrogen detection spectrum and is sufficiently sensitive, stable, and accurate for various applications.\n\nImportancePyrogen testing is mandatory and a critical method to ensure the safety of parenteral products including vaccines.\n\nCurrently, only two pharmacological tests, including the rabbit pyrogen test and the bacterial endotoxins test (BET), are applied to evaluate pyrogenic contamination in parenteral pharmaceuticals by most of state pharmacopoeias. Although generally reliable, both of these assays have shortcomings. The rabbit test is not quantitative but is expensive and involves the use of animals. It can also produce varying responses depending on the strain, age and housing conditions of the rabbits. The BET, however, does not detect pyrogens other than gram-negative bacterial endotoxins and is often problematic when used to test solutions with a high protein content.\n\nTo overcome these shortcomings and satisfy the growing need for new methods prompted by the constantly increasing production of biological compounds, it is necessary to develop the novel assay for pyrogen detection.\n\nHighlightsO_LIThis novel reporter gene assay can detect different types of pyrogens, including the lipopolysaccharide of gram-negative bacteria, the lipoteichoic acid of gram-positive bacteria, and the zymosan of fungi.\nC_LIO_LIThe novel reporter gene assay is sufficiently sensitive, stable, and accurate for various applications.\nC_LI

immunology↗