Biology subjects

Rajapakse, I.

Publications and source records attributed to Rajapakse, I..

14 recordsLinked to original sources

bioRxiv2026

An agent-based model of wound healing in fibroblast scratch assays

Wound healing assays are widely used to study fibroblast migration and proliferation in the lab. Although traditional scratch assays provide valuable insight, they can be time-intensive, inflexible, and difficult to reproduce. Computational models provide an efficient and reproducible way to study fibroblast behavior in silico. We developed an agent-based simulation of fibroblast scratch assays that models migration, proliferation, contact inhibition, cell-cell mechanical interactions, and the extracellular matrix (ECM) through a particle representation of fibroblasts and the ECM. To validate our model, we performed two tests: a leave-one-out test, where model parameters were calibrated to 6 imaging time series and tested on the remaining one, and a prediction test, where model parameters were calibrated to the first 24 hours of data and the remaining 66 hours of data were predicted. In our leave-one-out test, our model achieved a mean normalized root mean squared error (mean NRMSE) of 3.34%, and a Pearsons R of > 0.98 across all tested wound geometries time series. In the prediction test, the model predicted the remaining 66 hours of wound closure with an NRMSE of 2.08%. These results demonstrate that experimentally observed wound healing behavior of fibroblasts across different geometries can be modeled by local, agent-based rules. Our model provides an efficient platform for the experimentation and in silico study of fibroblast behavior.

systems biology↗

bioRxiv2026

ARCH3D: A foundation model for global genome architecture

Biological foundation models are transforming scientific discovery by creating information-rich representations that enable inference in low-data settings. Progress on these models has mainly been achieved by increasing input contextual information, e.g., base pairs or genes. Most work, however, focuses on DNA, RNA, and protein, leaving genome architecture, a fundamental component regulating processes like the cell cycle and cell-fate determination, underexplored. Here, we introduce ARCH3D: a foundation model for global genome architecture. ARCH3D uses a novel masked locus modeling task that increases input contextual information to include genome-wide contact profiles of loci spread across the entirety of the genome. We demonstrate this strategy captures global genome structure by showing ARCH3D embeddings preserve genomic spatial structure, reconstruct interchromosomal interactions under extreme sparsity, and enable identification of multi-way interactions. Ultimately, ARCH3D provides a potential structural foundation for building the virtual genome, an artificial intelligence-based model capable of simulating genome behavior and dynamics.

bioinformatics↗

bioRxiv2024

Data-guided direct reprogramming of human fibroblasts into the hematopoietic lineage

Direct reprogramming of human fibroblasts into hematopoietic stem cells (HSCs) offers a promising strategy for generating autologous cells to treat blood and immune disorders. Current protocols are limited by low efficiency and insufficient tools for evaluating reprogramming outcomes. Although functional assays are the standard for confirming cell identity, they require fully reprogrammed cells, limiting their utility during protocol development. To address this, we assembled a single-cell transcriptomic reference atlas of hematopoietic reprogramming and tested an algorithmically-predicted transcription factor recipe for HSC induction. Long-read single-cell RNA sequencing of CD34+ reprogrammed cells revealed progressive loss of fibroblast identity alongside induction of early hematopoietic and endothelial programs, with reference-atlas benchmarking placing reprogrammed cells in an intermediate transcriptomic state between fibroblasts, endothelial cells, and HSCs. Isoform-level analysis further revealed transcriptional remodeling not captured by gene-level analyses. This experimental-computational framework offers a generalizable strategy for characterizing partially reprogrammed states and guiding optimization of reprogramming protocols.

bioinformatics↗

bioRxiv2023

Automated In Vitro Wound Healing Assay

Restoring the epidermal barrier after injury requires spatial and temporal orchestration of migration, proliferation, and signaling across many cell types. The mechanisms that coordinate this complex process are incompletely understood. In vitro wound assays are common model systems for examining these mechanisms in wound healing. In the scratch assay, a cell-free gap is created by mechanical removal of cells from a monolayer, followed by monitoring cell migration into the gap over time. While simple and low-cost, manual scratch assays are limited by low reproducibility and low throughput. Here, we have designed a robotics-assisted automated wound healing (AWH) assay that increases reproducibility and throughput while integrating automated live-cell imaging and analysis. Wounds are designed as computer-aided design (CAD) models and recreated in confluent cell layers by the BioAssemblyBot (BAB) 3D-bioprinting platform. The dynamics of migration and proliferation in individual cells are evaluated over the course of wound closure using live-cell fluorescence microscopy and our high-performance image processing pipeline. The AWH assay outperforms the standard scratch assay with enhanced consistency in wound geometry. Our ability to create diverse wound shapes in any multi-well plate with the BAB not only allows for multiple experimental conditions to be analyzed in parallel but also offers versatility in the design of wound healing experiments. Our method emerges as a valuable tool for the automated completion and analysis of high-throughput, reproducible, and adaptable in vitro wound healing assays.

bioengineering↗

bioRxiv2022

Obesity disrupts innate-adaptive immune network patterning in adipose tissue

ObjectiveObesity-induced metabolic dysfunction, tissue remodelling, and chronic inflammation in visceral white adipose-tissue (WAT) are correlated with insulin resistance, type II diabetes, and metabolic disease pathogenesis [1]. In this work, we sought to establish spatio-temporal context of adipose tissue macrophage (ATM) reprogramming during obesity. MethodsWe captured single-cell RNA-sequencing, spatial transcriptomics, and histological imagining of murine WAT over the course of diet-induced obesity to study macrophage phenotype dynamics. We developed a straightforward mathematical approach to integrating multi-modal data to quantify obesityinduced changes to WAT organization. We aligned ATM phenotypes with crown-like structures (CLS) in early obesity and used spatial network analysis to uncover signalling mechanisms implicated in CLS formation. ResultsWe identified novel diversity of the lipid-associated macrophage (LAM) phenotype, whose transcriptional profile, signaling mechanisms, and spatial context serve as indicators of CLS formation in early obesity. We demonstrated that dysregulation of lipid-metabolic signalling is a critical turning point in the monocyte-LAM lineage and identified novel ligand-receptor mechanisms including Apoe, Lrp1, Lpl and App that serve as hallmarks of nascent CLS in WAT. ConclusionsMulti-modal spatio-temporal profiling demonstrates that LAMs disproportionately accumulate in CLS and are preceded by a transition-state macrophage phenotype with monocytic origins. We identified novel ligand-receptor interactions implicated in nascent CLS regions which may guide future cellular-reprogramming interventions for obesity-related sequelae. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=188 SRC="FIGDIR/small/482134v6_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@1d7ac5borg.highwire.dtl.DTLVardef@74c5c7org.highwire.dtl.DTLVardef@64b722org.highwire.dtl.DTLVardef@33392e_HPS_FORMAT_FIGEXP M_FIG C_FIG Highlights- We characterize a novel lipid-associated macrophage (LAM) phenotype along the monocyte-LAM lineage - Integrated imaging, single-cell sequencing and spatial transcriptomics data show that LAMs accumulate at nascent CLS - Analysis of spatial transcriptomics data reveals a novel set of ligands and receptors that implicate immature LAMs in shaping the CLS microenvironment in early obesity - We present a simple mathematical framework for studying dynamics of tissue-structure over time

cell biology↗

bioRxiv2021

Deciphering Multi-way Interactions in the Human Genome

Chromatin architecture, a key regulator of gene expression, can be inferred using chromatin contact data from chromosome conformation capture, or Hi-C. However, classical Hi-C does not preserve multi-way contacts. Here we use long sequencing reads to map genome-wide multi-way contacts and investigate higher order chromatin organization in the human genome. We use hypergraph theory for data representation and analysis, and quantify higher order structures in neonatal fibroblasts, biopsied adult fibroblasts, and B lymphocytes. By integrating multi-way contacts with chromatin accessibility, gene expression, and transcription factor binding, we introduce a data-driven method to identify cell type-specific transcription clusters. We provide transcription factor-mediated functional building blocks for cell identity that serve as a global signature for cell types.

bioinformatics↗

bioRxiv2021

Cohesin Releasing Factor WAPL Regulates Genome Structure and Function of Mature T Cells

The cohesin complex modulates gene expression and cellular functions by shaping three-dimensional (3D) organization of chromatin. WAPL, cohesins DNA release factor, regulates 3D chromatin architecture. The 3D genome structure and its relevance to mature T cell functions in vivo is not well understood. We show that in vivo lymphopenic expansion, and allo-antigen driven proliferation, alters the 3D structure and function of the genome in mature T cells. Conditional deletion of Wapl in T cells reduced long-range genomic interactions, altered chromatin A/B compartments and interactions within topologically associating domains (TADs) of the chromatin in T cells at baseline. Comparison of chromatin structure in normal and WAPL-deficient T cells after lymphopenic and allo-antigen driven stimulation revealed reduced loop extensions with changes in cell cycling genes. WAPL-mediated changes in 3D architecture of chromatin regulated activation, cycling and proliferation of T cells in vitro and in vivo. Finally, WAPL-deficient T cells demonstrated reduced severity of graft-versus-host disease (GVHD) following experimental allogeneic hematopoietic stem cell transplantation. These data collectively characterize 3D genomic architecture of T cells in vivo and demonstrate biological and clinical implications for its disruption by cohesin release factor WAPL.

molecular biology↗

bioRxiv2020

TCF7L2 Silencing Reprograms the 4D Nucleome of Colorectal Cancer Cells

Canonical Wnt signaling is crucial for intestinal homeostasis as the major Wnt signaling effector in the intestines, TCF4, is required for stem cell maintenance. The capability of TCF4 to maintain the stem cell phenotype is contingent upon {beta}-catenin, a potent transcriptional activator which interacts with histone acetyltransferases and chromatin remodeling complexes. In colorectal cancer, mutations result in high levels of nuclear {beta}-catenin causing aberrant cell growth. Here, we used RNAi to explore the influence of TCF4 on chromatin structure (Hi-C) and gene expression (RNA sequencing) across a 72-hour time series in colorectal cancer. We found that TCF4 reduction results in a disproportionate upregulation of gene expression genome-wide, including a powerful induction of SOX2. Hi-C analysis revealed a general increase in chromatin compaction across the entire time series, though this did not influence gene expression. Analysis of local chromosome organization demonstrated a TAD boundary loss which influenced the expression of a cluster of CEACAM genes on chromosome 19. Four-dimensional nucleome (4DN) analysis, which integrates structural (Hi-C) and functional (RNA sequencing) data, identified EMT and E2F as the two most deregulated pathways and LUM, TMPO, and AURKA as highly influential genes in these networks. Results from gene expression, chromatin structure, and centrality analyses were then integrated to generate a list of candidate transcription factors for reprogramming of colorectal cancer cells to a vulnerable state. The top ranked transcription factor in our analysis was c-JUN, an oncoprotein known to interact with TCF4 and {beta}-catenin.

cancer biology↗

bioRxiv2020

Genome architecture and transcription data reveal allelic bias during the cell cycle

Every human somatic cell inherits a maternal and a paternal genome, which work together to give rise to cellular phenotypes. However, the allele-specific relationship between gene expression and genome structure through the cell cycle is largely unknown. By integrating haplotype-resolved genome-wide chromosome conformation capture, mature and nascent mRNA, and protein binding data, we investigate this relationship both globally and locally. We introduce the maternal and paternal 4D Nucleome, enabling detailed analysis of the mechanisms and dynamics of genome structure and gene function for diploid organisms. Our analyses find significant coordination between allelic expression biases and local genome conformation, and notably absent expression bias in universally essential cell cycle and glycolysis genes. We propose a model in which coordinated biallelic expression reflects prioritized preservation of essential gene sets.

bioinformatics↗

bioRxiv2020

4DNvestigator: a toolbox for the analysis of time series Hi-C and RNA-seq data

Data on genome organization and output over time, or the 4D Nucleome (4DN), require synthesis for meaningful interpretation. Development of tools for the efficient integration of these data is needed, especially for the time dimension. We present the "4DNvestigator", a user-friendly network based toolbox for the analysis of time series genome-wide genome structure (Hi-C) and gene expression (RNA-seq) data. Additionally, we provide methods to quantify network entropy, tensor entropy, and statistically significant changes in time series Hi-C data at different genomic scales. Availabilityhttps://github.com/lindsly/4DNvestigator

bioinformatics↗

bioRxiv2018

Dynamic Network Analysis of the 4D Nucleome

MotivationFor many biological systems, it is essential to capture simultaneously the function, structure, and dynamics in order to form a comprehensive understanding of underlying phenomena. The dynamical interaction between 3D genome spatial structure and transcriptional activity creates a genomic signature that we refer to as the four-dimensional organization of the nucleus, or 4D Nucleome (4DN). The study of 4DN requires assessment of genome-wide structure and gene expression as well as development of new approaches for data analysis.\n\nResultsWe propose a dynamic multilayer network approach to study the co-evolution of form and function in the 4D Nucleome. We model the dynamic biological system as a temporal network with node dynamics, where the network topology is captured by chromosome conformation (Hi-C), and the function of a node is measured by RNA sequencing (RNA-seq). Network-based approaches such as von Neumann graph entropy, network centrality, and multilayer network theory are applied to reveal universal patterns of the dynamic genome. Our model integrates knowledge of genome structure and gene expression along with temporal evolution and leads to a description of genome behavior on a system wide level. We illustrate the benefits of our model via a real biological dataset on MYOD1-mediated reprogramming of human fibroblasts into the myogenic lineage. We show that our methods enable better predictions on form-function relationships and refine our understanding on how cell dynamics change during cellular reprogramming.\n\nAvailability: The software is available upon request.\n\nContactindikar@umich.edu\n\nSupplementary informationSee Supplementary Material.

bioinformatics↗

bioRxiv2017

Parental allele-specific genome architecture and transcription during the cell cycle

A normal human somatic cell inherits two haploid genomes. Individual chromosomes of each pair have distinct parental origins and parental alleles are known to unequally contribute to cellular function. We integrated chromosome conformation (form) and gene transcription (function) analyses to dissect the dynamics of the maternal and paternal genomes in lymphoblastoid cells during the cell cycle. We found a distinct set of homologous alleles with very different activity often located close to boundaries of euchromatin and heterochromatin domains. We also identified a set of allele-biased topologically associating domains (TADs) that were small sized and had higher gene density. Thousands of genes show allelically biased expression (ABE) with false discovery rate < 0.05, and 98% of them have no allelic switching during G1, S, and G2/M phases. A subset of ABE genes are preferentially localized near TAD boundaries, enriched with chromatin organization transcription factor binding sites, and contained higher number of sequence variants in CCCTC-binding factor sites. Our results extend previous findings of sequence variation as a basis for unequal functional parental genomes. Investigation of haplotype-resolved form-function dynamics may further our understanding of phenotypic traits, genetic diseases, vulnerability to complex disorders, and the development of cancers.

genomics↗

bioRxiv2017

An Algorithm for Cellular Reprogramming

The day we understand the time evolution of subcellular elements at a level of detail comparable to physical systems governed by Newtons laws of motion seems far away. Even so, quantitative approaches to cellular dynamics add to our understanding of cell biology, providing data-guided frameworks that allow us to develop better predictions about, and methods for, control over specific biological processes and system-wide cell behavior. In this paper, we describe an approach to optimizing the use of transcription factors (TFs) in the context of cellular reprogramming. We construct an approximate model for the natural evolution of a cell cycle synchronized population of human fibroblasts, based on data obtained by sampling the expression of 22,083 genes at several time points along the cell cycle. In order to arrive at a model of moderate complexity, we cluster gene expression based on the division of the genome into topologically associating domains (TADs) and then model the dynamics of the TAD expression levels. Based on this dynamical model and known bioinformatics, such as transcription factor binding sites (TFBS) and functions, we develop a methodology for identifying the top transcription factor candidates for a specific cellular reprogramming task. The approach used is based on a device commonly used in optimal control. Our data-guided methodology identifies a number of transcription factors previously validated for reprogramming and/or natural differentiation. Our findings highlight the immense potential of dynamical models, mathematics, and data-guided methodologies for improving strategies for control over biological processes.\n\nSignificance StatementReprogramming the human genome toward any desirable state is within reach; application of select transcription factors drives cell types toward different lineages in many settings. We introduce the concept of data-guided control in building a universal algorithm for directly reprogramming any human cell type into any other type. Our algorithm is based on time series genome transcription and architecture data and known regulatory activities of transcription factors, with natural dimension reduction using genome architectural features. Our algorithm predicts known reprogramming factors, top candidates for new settings, and ideal timing for application of transcription factors. This framework can be used to develop strategies for tissue regeneration, cancer cell reprogramming, and control of dynamical systems beyond cell biology.

bioinformatics↗

bioRxiv2017

Genome Architecture Leads a Bifurcation in Cell Identity

Genome architecture is important in transcriptional regulation and study of its features is a critical part of fully understanding cell identity. Altering cell identity is possible through overexpression of transcription factors (TFs); for example, fibroblasts can be reprogrammed into muscle cells by introducing MYOD1. How TFs dynamically orchestrate genome architecture and transcription as a cell adopts a new identity during reprogramming is not well understood. Here we show that MYOD1-mediated reprogramming of human fibroblasts into the myogenic lineage undergoes a critical transition, which we refer to as a bifurcation point, where cell identity definitively changes. By integrating knowledge of genome-wide dynamical architecture and transcription, we found significant chromatin reorganization prior to transcriptional changes that marked activation of the myogenic program. We also found that the local architectural and transcriptional dynamics of endogenous MYOD1 and MYOG reflected the global genomic bifurcation event. These TFs additionally participate in entrainment of biological rhythms. Understanding the system-level genome dynamics underlying a cell fate decision is a step toward devising more sophisticated reprogramming strategies that could be used in cell therapies.

cell biology↗