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Results for “Systems Biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2025

Protein Compositional Ratio Representation (PCRR)Systematically Improves Human Disease Prediction

Plasma proteomics captures a functional snapshot of human physiology; yet, most machine learning models treat protein abundances as independent variables, ignoring the fact that biological systems and proteomic measurements are inherently compositional. Many molecular processes depend not on absolute concentrations but on relative balances: receptor-ligand stoichiometry, enzyme-substrate ratios, and homeostatic feedbacks that govern signaling and metabolism. We propose that these relationships are best captured through pairwise protein ratios, which more faithfully reflect underlying biochemical constraints than raw expression values. We evaluate a machine learning framework that models pairwise log-ratios of proteins (log(A) -log(B)) as features, thereby encoding compositional structure directly into the learning space. Applied to the ROSMAP plasma proteomics cohort (n = 871), this approach substantially improved the classification of Alzheimers subtypes (NCI, MCI, AD, AD+) with an average AUROC gain of +0.1274 over a strong baseline that incorporated raw proteomics and demographics. The top-ranked ratios (e.g., SEMA3C:TMEM70, IDUA:NPTXR) captured converging pathogenic pillars of Alzheimers disease, including microglial activation, proteostasis dysregulation, and lipid-clearance imbalance, highlighting that ratio-based features recover biologically coherent axes of disease. To assess generality, we scaled the method to the UK Biobank proteomic dataset (n > 53,000; 587 phenotypes). The ratio-based model outperformed raw-level models in 95.1 % of diseases, with statistically significant (FDR < 0.05) gains in 56.7 %. Together, these results suggest that proteomic data should be viewed and modeled as compositional systems, where relative protein abundances carry the accurate functional signal. This insight supports the broader utility of ratio-based representations for disease prediction and biomarker discovery.

bioinformatics↗
bioRxiv2024

Mechanochemical Patterning Localizes the Organizer of a Luminal Epithelium

The spontaneous emergence of tissue patterns is often attributed to biochemical reaction-diffusion systems. In Hydra tissue regeneration, the formation of a Wnt signaling center serves as a well-known example of such a process. However, despite extensive research, a strictly biochemical mechanism for self-organization in Hydra remains elusive. In this study, we investigated mechanical stimuli and identified a positive feedback loop between Wnt signaling and tissue stretching. We developed a mathematical model of mechanochemical pattern formation in a closed elastic shell, representing regenerating Hydra epithelial spheroids. Our model explains how mechanical forces drive axis formation and predicts the organizers location under various perturbations, providing a more comprehensive understanding of the spatial dynamics involved. Validation by partially confining regenerating tissues showed that the organizer indeed forms in regions with the greatest stretching potential. This work highlights a novel mechanochemical mechanism for luminal epithelium patterning, suggesting that mechanical forces, in addition to biochemical signals, play a crucial role in tissue regeneration and axis specification. Our findings offer broader implications for the role of mechanical forces in tissue organization in various biological systems, opening new avenues for investigating mechanochemical feedback in development and regeneration.

developmental biology↗
bioRxiv2020

Single Cell Analysis of Regions of Interest (SCARI) using a novel photoswitchable tag

The functional activity and differentiation potential of cells is determined by their interaction with surrounding cells. Approaches that allow the unbiased characterization of cell states while at the same time providing spatial information are of major value to assess this environmental influence. However, most current techniques are hampered by a trade-off between spatial resolution and cell profiling depth. Here, we developed a photoswitch-based technology that allows the isolation and in-depth analysis of live cells from regions of interest in complex ex vivo systems, including human tissues. The use of a highly sensitive 4-nitrophenyl(benzofuran)-cage coupled to nanobodies allowed photoswitching of cells in areas of interest with low-intensity violet light and without detectable phototoxicity. Single cell RNA sequencing of spatially defined CD8+ T cells was used to exemplify the feasibility of identifying location-dependent cell states at the single cell level. Finally, we demonstrate the efficient labeling and photoswitching of cells in live primary human tumor tissue. The technology described here provides a valuable tool for the analysis of spatially defined cells in diverse biological systems, including clinical samples.

bioengineering↗
bioRxiv2022

scDREAMER: atlas-level integration of single-cell datasets using deep generative model paired with adversarial classifier

Integration of heterogeneous single-cell sequencing datasets generated across multiple tissue locations, time and conditions is essential for a comprehensive understanding of the cellular states and expression programs underlying complex biological systems. Here, we present scDREAMER (https://github.com/Zafar-Lab/scDREAMER), a novel data integration framework that employs a novel adversarial variational autoencoder for learning lower-dimensional cellular embeddings and a batch classifier neural network for the removal of batch effects. Using five real benchmarking datasets, we demonstrated that scDREAMER can overcome critical challenges including the presence of skewed cell types among batches, nested batch effects, large number of batches and conservation of development trajectory across different batches. Moreover, our benchmarking demonstrated that scDREAMER outperformed state-of-the-art methods in batch-correction and conservation of biological variation. Using a 1 million cells dataset, we further showed that scDREAMER is scalable and can perform atlas-level integration across species (e.g., human and mouse) while being faster than other deep-learning-based methods.

bioinformatics↗
bioRxiv2025

Comparative analysis of shared and unique mechanisms important for diverse strains of Pasteurella multocida to cause systemic infection in mice

Pasteurella multocida is a Gram-negative bacterium that causes a range of distinct diseases in livestock animals. Different P. multocida diseases are associated with different capsule and lipopolysaccharide (LPS) types, but little else is known about what underpins this disease specificity. In this study, we utilised transposon-directed insertion site sequencing (TraDIS) to identify genes in two diverse P. multocida strains that are required for growth in rich media, and genes important for survival during systemic infections in BALB/c mice. Analysis of growth in heart infusion broth showed that the fowl cholera isolate VP161 (capsule type A and LPS type L1) and the bovine haemorrhagic septicaemia isolate M1404 (capsule type B and LPS type L2) shared 461 genes essential for growth in rich media, with 95% of these present in all publicly available closed P. multocida genomes. In vivo fitness analysis identified 63 and 94 genes important for VP161 and M1404 survival in BALB/c mice, respectively. Only 35 homologs were identified as important for survival in both strains, showing that conserved biological systems can be differentially important for different P. multocida strains. Investigation of proteins involved in the catabolite response showed that an active cyclic-adenosine monophosphate (cAMP) receptor protein (CRP) was required for maximal fitness in M1404. Furthermore, disrupting CRP or cAMP production also reduced capsule production in M1404, but increased capsule production in VP161, demonstrating that these P. multocida strains have different regulatory systems for crucial virulence factors. Author SummaryPasteurella multocida is an important livestock pathogen, causing several distinct severe diseases in many different livestock animals. P. multocida can spread rapidly throughout animal populations, with peracute infections causing death within 48 h, resulting in large outbreaks with high mortality. Host predilection and disease presentation often correlate with the capsule and lipopolysaccharide type produced by the causative strain. However, the processes that allow certain strains to cause a particular disease are not well understood. In this study, we have comprehensively identified genes required for two P. multocida strains to cause systemic infection in mice, showing that these diverse strains have differential requirements for survival during a systemic infection. This information is crucial for understanding P. multocida diseases and for the development of new strategies to combat infection.

microbiology↗
bioRxiv2025

WormAI: Artificial Intelligence of Networks for Nematode Phenotyping

The nematode Caenorhabditis elegans is a key model organism in biological research due to its genetic similarity to humans and its utility in studying complex processes. Traditional image analysis methods, such as those using ImageJ, are labour-intensive, which has led to the integration of AI. This study introduces an AI framework with three machine learning models: WormGAN, a Generative Adversarial Network for generating synthetic nematode images to enhance training data; WormDET, for precise movement tracking; and WormREG, for accurate anatomical measurements. Together, these tools significantly improve the efficiency and accuracy of phenotypic analysis. WormAI demonstrates substantial potential for high-throughput dataset analysis, advancing research in systems biology, drug discovery, and aging. This framework streamlines workflows, enabling faster and more precise discoveries in C. elegans studies.

bioinformatics↗
bioRxiv2022

Systematic histone H4 replacement in Arabidopsis thaliana reveals a role for H4R17 in regulating flowering time

Despite the broad array of roles for epigenetic mechanisms on regulating diverse processes in eukaryotes, no experimental system for the direct assessment of histone function is currently available in plants. In this work, we present the development of a genetic strategy in Arabidopsis thaliana in which modified H4 transgenes can completely replace the expression of endogenous histone H4. Using this strategy, we established a collection of plants expressing different H4 point mutants targeting residues that may be post-translationally modified in vivo. To demonstrate the utility of this new H4 mutant collection, we screened it to uncover substitutions in H4 that alter flowering time. We identified different mutations in the tail (H4R17A) and the globular domain (H4R36A, H4R39K, H4R39A, and H4K44A) of H4 that strongly accelerate the floral transition. Furthermore, we found a conserved regulatory relationship between H4R17 and the ISWI chromatin remodeling complex in plants. Similar to other biological systems, H4R17 regulates nucleosome spacing via ISWI. Overall, this work provides a large set of H4 mutants to the plant epigenetics community that can be used to systematically assess histone H4 function in A. thaliana and a roadmap to replicate this strategy for studying other histone proteins in plants.

plant biology↗
bioRxiv2026

Dynamic engagement of dual-role regulators by the Sin3 complex

Gene expression is governed by dynamic switches between repressive and activating transcriptional states1,2. Among the molecules mediating these transitions, chromatin readers and transcription factors play pivotal roles3,4. However, how they assemble with regulatory machineries to enable crosstalk between gene repression and activation remains unknown. Here, we use an integrative structural dynamics approach - combining cryo-EM, crosslinking mass spectrometry, fragment-resolved protein interactome mapping and crystallography - to show how the dual-role chromatin reader Cti6 and transcription factors Ash1 and Ume6 engage the Sin3 deacetylase complex, a major regulatory hub in eukaryotes5. We find that Cti6 competes with Ash1 to drive its dynamic recruitment to a shared peripheral module, while Ume6 engages the Sin3 scaffold through a defined, minimal interface. Using high-throughput mutational scanning, we reveal deleterious and gain-of-function mutations in Sin3, identifying evolutionarily conserved residues essential for anchoring transcription factors. Together, these results provide structural and functional insights into how dual-role regulators engage the central Sin3 complex, revealing subtle assembly principles that may facilitate crosstalk between gene repression and activation. They also establish an integrative multidisciplinary framework to dissect the dynamics of macromolecular assemblies across biological systems.

molecular biology↗
bioRxiv2025

Generalizable prediction of liquid-liquid phase separation from protein sequence

Liquid-liquid phase separation (LLPS) is emerging as a fundamental process supporting multiple facets of biological systems. This phenomenon enables the dynamic compartmentalization of biomolecules contributing to a wide range of cellular functions, though in many instances its precise role and evolution remain unclear. Protein phase separation naturally occurs within cells and is prevalent across all species. Despite a recent surge in protein LLPS discovery, current predictive models lack generalizability and fail to identify the full spectrum of phase-separating proteins. To address this shortcoming, we developed Phaseek, a hybrid model integrating contextual sequence encoding with statistical graph representations to score LLPS propensity of amino acid sequences. Phaseek accurately identifies phase-separating proteins across diverse biological contexts, predicting key functional regions and the effects of point mutations. Proteome-wide predictions for 18 species highlight important physicochemical features. Gene Ontology enrichments recapitulate known processes (e.g., nucleic acid binding, nuclear localization, chromatin organization) and suggest novel areas of investigation. Phylogenetic analysis of orthologs further suggests that LLPS is evolutionarily conserved beyond sequence similarity. In addition, we used Phaseek to design de novo phase-separating peptides and achieved a 70% success rate in vivo. Provided with a user-friendly implementation, Phaseek serves as a multipurpose LLPS predictor for advancing both fundamental and applied LLPS research.

molecular biology↗
bioRxiv2026

Energetically constrained turnover drives the emergence of aging

Aging is characterized by progressive functional decline, yet why such decline is observed broadly across living systems remains unclear. While molecular and cellular mechanisms describe how aging progresses, they do not explain why functional decline should arise as a natural consequence of living organization. Here, we show that aging naturally emerges from three general features of life: unavoidable damage, turnover-mediated maintenance, and the energetic constraint of turnover. We develop a hierarchical damage-turnover model in which component-level damage and energetically constrained turnover jointly determine whole-system performance. In the model, damage stochastically converts functional components into non-functional components, whereas turnover restores component performance at a rate coupled to whole-system performance. Analytical and Monte Carlo analyses reveal two regimes: a non-aging regime, in which performance remains finite, and an aging regime, in which performance progressively collapses toward zero. Performance-independent turnover always maintains a positive steady state, whereas performance-dependent turnover generates irreversible decline when reduced performance weakens maintenance capacity. Stochastic fluctuations further promote collapse near the transition boundary, even when deterministic analysis predicts a nonzero steady state. These results indicate that unavoidable damage and energetically constrained turnover are sufficient to generate aging-like decline, providing a minimal theoretical explanation for long-term irreversibility in biological systems.

biophysics↗
bioRxiv2019

Rational Design of a Bifunctional AND-Gate Ligand to Modulate Cell-Cell Interactions

Protein \"AND-gate\" systems, in which a ligand acts only on cells with two different receptors, direct signaling activity to a particular cell type and avoid action on other cells. In a bifunctional AND-Gate protein, the molecular geometry of the protein domains is crucial. Here we constructed a tissue-targeted erythropoietin (EPO) that stimulates red blood cell (RBC) production without triggering thrombosis. EPO was directed to RBC precursors and mature RBCs by fusion to an anti-glycophorin A antibody V region. Many such constructs activated EPO receptors in vitro and stimulated RBC and not platelet production in mice but nonetheless enhanced thrombosis in mice and caused adhesion between RBCs and EPO receptor-bearing cells. Based on a protein-structural model of the RBC surface, we rationally designed an anti-glycophorin/EPO fusion that does not induce cell adhesion in vitro or enhance thrombosis in vivo. Thus, meso-scale geometry can inform design of synthetic-biological systems.\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=138 SRC=\"FIGDIR/small/711549v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (22K):\norg.highwire.dtl.DTLVardef@93b382org.highwire.dtl.DTLVardef@ee37ccorg.highwire.dtl.DTLVardef@1913fe4org.highwire.dtl.DTLVardef@f998b1_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗
bioRxiv2024

Integrative analysis of single-cell gene expression: A comprehensive database approach

The exponential growth of single-cell datasets provides unprecedented opportunities to advance our understanding of complex biological systems. However, effectively locating and integrating related studies for meaningful insights remains challenging. Traditional databases primarily index basic metadata, which necessitates time-consuming downloading and re-filtering based on gene expression and cell type or tissue composition, followed by computationally intensive aggregation. This process often results in excessively large datasets that are difficult to analyze effectively, further complicated by batch effects. To address these issues, we have developed a computational approach to efficiently extract and index both expression data and annotations. Our comprehensive database incorporates detailed author annotations and gene expression profiles, enabling refined searches and integrated analyses to uncover common biological patterns while accounting for the repeatability of patterns across multiple studies and mitigating batch effects. This approach significantly reduces computational demands and enhances the accessibility and utility of single-cell transcriptomics data for the broader research community. In the first version, we release a human database comprising 244 datasets from 236 cell types, 35 tissues, and 31 conditions.

bioinformatics↗
bioRxiv2025

cellSight: Characterizing dynamics of cells using single-cell RNA-sequencing

Single-cell analysis has transformed our understanding of cellular diversity, offering insights into complex biological systems. Yet, manual data processing in single-cell studies poses challenges, including inefficiency, human error, and limited scalability. To address these issues, we propose the automated workflow cellSight, which integrates high-throughput sequencing in a user-friendly platform. By automating tasks like cell type clustering, feature extraction, and data normalization, cellSight reduces researcher workload, promoting focus on data interpretation and hypothesis generation. Its standardized analysis pipelines and quality control metrics enhance reproducibility, enabling collaboration across studies. Moreover, cellSights adaptability supports integration with emerging technologies, keeping pace with advancements in single-cell genomics. cellSight accelerates discoveries in single-cell biology, driving impactful insights and clinical translation. It is available with documentation and tutorials at https://github.com/omicsEye/cellSight.

bioinformatics↗
bioRxiv2018

Evolutionarily informed deep learning methods: Predicting transcript abundance from DNA sequence

Deep learning methodologies have revolutionized prediction in many fields, and show potential to do the same in molecular biology and genetics. However, applying these methods in their current forms ignores evolutionary dependencies within biological systems and can result in false positives and spurious conclusions. We developed two novel approaches that account for evolutionary relatedness in machine learning models: 1) gene-family guided splitting, and 2) ortholog contrasts. The first approach accounts for evolution by constraining the models training and testing sets to include different gene families. The second, uses evolutionarily informed comparisons between orthologous genes to both control for and leverage evolutionary divergence during the training process. The two approaches were explored and validated within the context of mRNA expression level prediction, and have prediction auROC values ranging from 0.72 to 0.94. Model weight inspections showed biologically interpretable patterns, resulting in the novel hypothesis that the 3 UTR is more important for fine tuning mRNA abundance levels while the 5 UTR is more important for large scale changes.

molecular biology↗
bioRxiv2017

Integrative genomics study of microglial transcriptome reveals effect of DLG4 (PSD95) on white matter in preterm infants.

Preterm birth places newborn infants in an adverse environment that leads to brain injury linked to neuroinflammation. To characterise this pathology, we present a translational bioinformatics investigation, with integration of human and mouse molecular and neuroimaging datasets to provide a deeper understanding of the role of microglia in preterm white matter damage. We examined preterm neuroinflammation in a mouse model of encephalopathy of prematurity induced by IL1B exposure, carrying out a gene network analysis of the cell-specific transcriptomic response to injury, which we extended to analysis of protein-protein interactions, transcription factors, and human brain gene expression, including translation to preterm infants by means of imaging-genetics approaches in the brain. We identified the endogenous synthesis of DLG4 (PSD95) protein by microglia in mouse and human, modulated by inflammation and development. Systemic genetic variation in DLG4 was associated with structural features in the preterm infant brain, suggesting that genetic variation in DLG4 may also impact white matter development and inter-individual susceptibility to injury.\n\nPreterm birth accounts for 11% of all births 1, and is the leading global cause of deaths under 5 years of age 2. Over 30% of survivors experience motor and/or cognitive problems from birth 3, 4, which last into adulthood 5. These problems include a 3-8 fold increased risk of symptoms and disorders associated with anxiety, inattention and social and communication problems compared to term-born infants 6. Prematurity is associated with a 4-12 fold increase in the prevalence of Autism Spectrum Disorders (ASD) compared to the general population 7, as well as a risk ratio of 7.4 for bipolar affective disorder among infants born below 32 weeks of gestation 8.\n\nThe characteristic brain injury observed in contemporary cohorts of preterm born infants includes changes to the grey and white matter tissues, that specifically include oligodendrocyte maturation arrest, hypomyelination and cortical changes visualised as decreases in fractional anisotropy 9-13. Exposure of the fetus and postnatal infant to systemic inflammation is an important contributing factor to brain injury in preterm born infants 12, 14, 15, and the persistence of inflammation is associated with poorer neurological outcome 16. Sources of systemic inflammation include maternal/fetal infections such as chorioamnionitis (which it is estimated affects a large number of women at a sub-clinical level), with the effect of systemic inflammation in the brain being mediated predominantly by the microglial response 17.\n\nMicroglia are unique yolk-sac derived resident phagocytes of the brain 18, 19, found preferentially within the developing white matter as a matter of normal developmental migration 12. Microglial products associated with white matter injury include pro-inflammatory cytokines, such as interleukin-1{beta} (IL1B) and tumour necrosis factor (TNF-)20, which can lead to a sub-clinical inflammatory situation associated with unfavourable outcomes 21. In addition to being key effector cells in brain inflammation, they are critical for normal brain development in processes such as axonal growth and synapse formation 22, 23. The role of microglia in neuroinflammation is dynamic and complex, reflected in their mutable phenotypes including both pro-inflammatory and restorative functions 24. Despite their important neurobiological role, the time course and nature of the microglial responses in preterm birth are currently largely unknown, and the interplay of inflammatory and developmental processes is also unclear. We, and others, believe that a better understanding of the molecular mechanisms underlying microglial function could harness their beneficial effects and mitigate the brain injury of prematurity and other states of brain inflammation25, 26\n\nA clinically relevant experimental mouse model of IL1B-induced systemic inflammation has been developed to study the changes occurring in the preterm human brain 27, 28. This model recapitulates the hallmarks of encephalopathy of prematurity including oligodendrocyte maturation delay with consequent dysmyelination, associated magnetic resonance imaging (MRI) phenotypes and behavioural deficits. Here, we take advantage of this model system to characterise the molecular underpinnings of the microglial response to IL1B-driven systemic inflammation and investigate its role in concurrent development.\n\nIn preterm infants MRI is used extensively to provide in-vivo correlates of white and grey matter pathology, allowing clinical assessment and prognostication. Diffusion MRI (d-MRI) measures the displacement of water molecules in the brain, and provides insight into the underlying tissue structure. Various d-MRI measures of white matter have been associated with developmental outcome in children born preterm 29-32, with up to 60% of inter-individual variability in structural and functional features attributable to genetic factors 33, 34. White matter abnormalities are linked to associated grey matter changes at both the imaging and cellular level 10, 35, 36, with functional and structural consequences lasting into adulthood 37, 38. Tract Based Statistics (TBSS) allows quantitative whole-brain white matter analysis of d-MRI data at the voxel level while avoiding problems due to contamination by signals arising from grey matter 39. This permits voxel-wise statistical testing and inferences to be made about group differences or associations with greater statistical power. TBSS has been shown to be an effective tool for studying white matter development and injury in the preterm brain 40, providing a macroscopic in vivo quantitative measure of white matter integrity that is associated with cognitive, fine motor, and gross motor outcome 11, 41, 42.\n\nIn this work we take a translational systems biology approach to investigate the role of microglia in preterm neuroinflammation and brain injury. We integrate microglial cell-type specific data from a mouse model of perinatal neuroinflammatory brain injury with experimental ex vivo and in vitro validation, translation to the human brain across the lifespan including analysis of human microglia, and assessment of the impact of genetic variation on structure of the preterm brain. We add to the understanding of the neurobiology of prematurity by: a) revealing the endogenous expression of DLG4 (PSD95) by microglia in early development, which is modulated by developmental stage and inflammation; and b) finding an association between systemic genetic variability in DLG4 and white matter structure in the preterm neonatal brain.

genomics↗
bioRxiv2018

Genome-Wide Association Studies of malaria susceptibility and resistance: progress, pitfalls and prospects

P. falciparum malaria is still among the leading causes of child mortality in sub-Saharan Africa; killing hundreds of thousands of children each year. Malaria has been recognized as one of the prominent evolutionary selective forces of human genome that led to the emergence of multiple host protective polymorphisms associated with minimizing the risk of developing severe malaria in endemic areas. A comprehensive understanding of the genetic bases of malaria resistance can shed light to the molecular mechanisms of host-parasite interactions that can potentially pave ways to the development of new therapeutics and vaccines. Genome-wide association studies (GWASs) have recently been implemented in malaria endemic areas and identified a number of novel association genetic variants. Despite this success, only few variants did replicate across the studies and the underlying biology is yet to be understood for the majority of the novel variants. Besides, there are several open questions around heritability, polygenic effects, epistatic interactions, genetic correlations and associated molecular pathways among others. In this review, we first assess the progress and pitfalls of malaria susceptibility GWASs. We then, provide an overview of the current progress in post-GWAS approaches and discuss how these approaches can potentially be implemented in malaria susceptibility GWASs to extract further functional information. We conclude by highlighting the importance of multi-step and multidimensional integrative studies for unravelling the genetic basis of malaria susceptibility and resistance at systems biology level.

genomics↗
bioRxiv2023

Simple droplet microfluidics platform for drug screening on cancer spheroids

3D in vitro biological systems are progressively replacing 2D systems to increase the physiological relevance of cellular studies. Microfluidics-based approaches can be powerful tools towards such biomimetic systems, but often require high-end complicated and expensive processes and equipments for microfabrication. Herein, a drug screening platform is proposed, minimizing technicality and manufacturing steps. It provides an alternate way of spheroid generation in droplets in tubes. Droplets microfluidics then elicit multiple droplets merging events at programmable times, to submit sequentially the spheroids to chemotherapy and to reagents for cytotoxicity screening. After a comprehensive study of tumorigenesis within the droplets, the system is validated for drug screening (IC50) with chemotherapies in cancer cell lines as well as cells from patient-derived-xenografts (PDX). As compared to microtiter plates methods, our sytem reduces the initial amout of cell up to 10 times and opens new avenues towards primary tumors drug screening approaches.

cancer biology↗
bioRxiv2024

TrimNN: Characterizing cellular community motifs for studying multicellular topological organization in complex tissues

The spatial organization of cells plays a pivotal role in shaping tissue functions and phenotypes in various biological systems and diseased microenvironments. However, the topological principles governing interactions among cell types within spatial patterns remain poorly understood. Here, we present the Triangulation Cellular Community Motif Neural Network (TrimNN), a graph-based deep learning framework designed to identify conserved spatial cell organization patterns, termed Cellular Community (CC) motifs, from spatial transcriptomics and proteomics data. TrimNN employs a semi-divide-and-conquer approach to efficiently detect over-represented topological motifs of varying sizes in a triangulated space. By uncovering CC motifs, TrimNN reveals key associations between spatially distributed cell-type patterns and diverse phenotypes. These insights provide a foundation for understanding biological and disease mechanisms and offer potential biomarkers for diagnosis and therapeutic interventions.

bioinformatics↗