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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 37 records · Page 2Linked to original sources

bioRxiv2014

Quantitative comparison of single-cell sequencing methods using hippocampal neurons

Single-cell genomic analysis has grown rapidly in recent years and will find widespread applications in various fields of biology, including cancer biology, development, immunology, pre-implantation genetic diagnosis, and neurobiology. In this study, we amplified genomic DNA from individual hippocampal neurons using one of three single-cell DNA amplification methods (multiple annealing and looping-based amplification cycles (MALBAC), multiple displacement amplification (MDA), and GenomePlex whole genome amplification (WGA4)). We then systematically evaluated the genome coverage, GC-bias, reproducibility, and copy number variations among individual neurons. Our results showed that single-cell genome sequencing results obtained from the MALBAC and WGA4 methods are highly reproducible and have a high success rate. Chromosome-level and subchromosomal-level copy number variations among individual neurons can be detected.

Genomics↗

bioRxiv2014

Quantitative comparison of single-cell sequencing methods using hippocampal neurons

Genomics↗

bioRxiv2023

Evolution of intracellular free radical load in colon adenocarcinoma cells over the course of butyrate-induced redifferentiation

Fluorescent nanodiamonds have exceptional optical properties and are highly biocompatible, which allows to use them as labels for long-term tracking of the cells. The research fields that make use of this application of nanodiamonds include stem cell biology and cancer biology, where quiescent and differentiating cells can be traced in vitro and in vivo. However, these studies focus on using nanodiamonds as simple labels, whereas they can serve as highly sensitive intracellular sensors for free radical species. In this study, we aimed to bring the two approaches together and to assess the free radical production in the cells over the course of their differentiation. We report on the successful enterocytic differentiation of HT-29 colon adenocarcinoma cells, pre-loaded with fluorescent nanodiamonds. The cells were cultured in butyrate-free or butyrate-supplemented medium for 13 days. Butyrate-treated cells developed the morphological and molecular traits, characteristic for normal enterocytes. Fluorescent nanodiamonds did not have a negative effect on the process of differentiation. Moreover, the particles could be found in the cytoplasm of both undifferentiated and re-differentiated cells even after 13 days of culture. The internalized nanodiamonds were used to assess the free radical load in the undifferentiated and re-differentiated HT-29 cells at different stages of the experiment. Consistently with previous findings, re-differentiated HT-29 cells showed higher free radical load than undifferentiated ones.

cell biology↗

bioRxiv2021

Navigating Multi-scale Cancer Systems Biology towards Model-driven Personalized Therapeutics

Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built on top of this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- or multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multiscale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes by highlighting that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.

systems biology↗

bioRxiv2019

Exploring Integrative Analysis using the BioMedical Evidence Graph

The analysis of cancer biology data involves extremely heterogeneous datasets including information from RNA sequencing, genome-wide copy number, DNA methylation data reporting on epigenomic regulation, somatic mutations from whole-exome or whole-genome analyses, pathology estimates from imaging sections or subtyping, drug response or other treatment outcomes, and various other clinical and phenotypic measurements. Bringing these different resources into a common framework, with a data model that allows for complex relationships as well as dense vectors of features, will unlock integrative analysis. We introduce a graph database and query engine for discovery and analysis of cancer biology, called the BioMedical Evidence Graph (BMEG). The BMEG is unique from other biological data graphs in that sample level molecular information is connected to reference knowledge bases. It combines gene expression and mutation data, with drug response experiments, pathway information databases and literature derived associations. The construction of the BMEG has resulted in a graph containing over 36M vertices and 29M edges. The BMEG system provides a graph query based API to enable analysis, with client code available for Python, Javascript and R, and a server online at bmeg.io. Using this system we have developed several forms of integrated analysis to demonstrate the utility of the system. The BMEG is an evolving resource dedicated to enabling integrative analysis. We have demonstrated queries on the system that illustrate mutation significance analysis, drug response machine learning, patient level knowledge base queries and pathway level analysis. We have compared the resulting graph to other available integrated graph systems, and demonstrated that it is unique in the scale of the graph and the type of data it makes available.\n\nHighlightsO_LIData resource connected extremely diverse set of cancer data sets\nC_LIO_LIGraph query engine that can be easily deployed and used on new datasets\nC_LIO_LIEasily installed python client\nC_LIO_LIServer online at bmeg.io\nC_LI\n\nSummaryThe analysis of cancer biology data involves extremely heterogeneous datasets including information. Bringing these different resources into a common framework, with a data model that allows for complex relationships as well as dense vectors of features, will unlock integrative analysis. We introduce a graph database and query engine for discovery and analysis of cancer biology, called the BioMedical Evidence Graph (BMEG). The construction of the BMEG has resulted in a graph containing over 36M vertices and 29M edges. The BMEG system provides a graph query based API to enable analysis, with client code available for Python, Javascript and R, and a server online at bmeg.io. Using this system we have developed several forms of integrated analysis to demonstrate the utility of the system.

cancer biology↗

bioRxiv2025

EGR1 drives sex-differences in glioblastoma tumorigenicity

Although progress has been made in treating glioblastoma (GBM), with fewer than 5% of patients surviving more than 5 years after diagnosis. For reasons that are not well understood, females are roughly 60% as likely as males to develop GBM, and female patients consistently respond better to treatment than do males. Understanding the molecular etiology of these sex differences in tumor progression and resiliency to treatment could reveal potent new therapeutic targets, ultimately improving survival of both male and female GBM patients. Here we show that the transcription factor Egr1 is a primary mediator of sex differences in multiple GBM tumorigenic phenotypes. In multivariate analysis, high levels of EGR1 expression are correlated with shortened survival for male GBM patients only. To investigate the molecular mechanisms underlying this sex difference, we performed a genomic analysis in our established ex vivo murine model of sex differences, which showed that the transcription factors Egr1 and Klf5 preferentially recruit the transcriptional activator Brd4 to enhancers in male cells relative to female cells, explaining a previously made observation that Brd4 inhibitors reverse sex differences in GBM. Next, using murine and human primary GBM cells, we demonstrated that the small molecule compound SR18662, which downregulates Egr1 and its downstream target Klf5, abrogates GBM growth, migration, invasion, clonogenicity, and response to radiation in a sex-biased fashion. Finally, we knocked down Egr1 and Klf5 via CRISPRi in both untreated and SR18662-treated GBM cells to reveal the sex-biased anti-tumorigenic effects of SR18662 were largely due to Egr1 downregulation, independent of Klf5 downregulation. This result was replicated in vivo. Our results strongly indicate that an Egr1 regulon is a key determinant of sex differences in GBM. As EGR1 is implicated in the cancer biology of many cancers that also display sex differences in incidence and treatment response, our results are likely to be broadly applicable in oncology.

cancer biology↗

bioRxiv2023

Spatial cancer systems biology resolves heterotypic interactions and identifies disruption of spatial hierarchy as a pathological driver event

Spatially annotated single-cell datasets provide unprecedented opportunities to dissect cell-cell communication in development and disease. Heterotypic signaling includes interactions between different cell types and is well established in tissue development and spatial organization. Epithelial organization requires several different programs that are tightly regulated. Planar cell polarity (PCP) is the organization of epithelial cells along the planar axis, orthogonal to the apical-basal axis. Here, we investigate PCP factors and explore the implications of developmental regulators as malignant drivers. Utilizing cancer systems biology analysis, we derive a gene expression network for WNT-ligands (WNT) and their cognate frizzled (FZD) receptors in skin cutaneous melanoma. The profiles supported by unsupervised clustering of multiple-sequence alignments identify ligand-independent signaling and implications for metastatic progression based on the underpinning developmental spatial program. Omics studies and spatial biology connect developmental programs with oncological events and explain key spatial features of metastatic aggressiveness. Dysregulation of prominent PCP factors such as specific representatives of the WNT and FZD families in malignant melanoma recapitulates the development program of normal melanocytes but in an uncontrolled and disorganized fashion.

cancer biology↗

bioRxiv2024

Assessing Long-Term Stored Tissues for Multi-Omics Data Quality and Proteogenomics Suitability

As research into the complexities of cancer biology deepens, the integration of multi-omics analyses has emerged as a powerful approach to unravel the complex molecular basis of cancers. However, challenges related to sample availability, including size, collection procedures, and storage duration, hinder the broad application of this methodology. Despite these limitations, there is a growing interest in exploring the potential of archived samples to expand the scope of multi-omics research. Our study aims to investigate the impact of storage duration on the measurment in genomic, transcriptomic, and proteomic profiles of archived samples, demonstrating their viability for advancing our understanding of cancer biology. To comprehensively address these trends and limitations, we systematically examined archived samples collected over a decade, focusing on their transcriptomic, proteomic, and phosphoproteomic attributes. Analysis revealed intricate patterns and dynamic shifts, especially in long-term transcriptomic data, with observed declines in read counts related to protein coding and gene coverage. However, these changes did not compromise the fundamental gene expression landscape. Proteomic result also demonstrated that storage period did not significantly influence proteomic measurement. Comparisons of housekeeping gene (HKG) and housekeeping protein (HKP) expressions unveiled consistent transcriptomic levels across samples, while distinctive proteomic disparities between tumor and normal tissues. In conclusion, the challenges posed by limited sample availability in multi-omics studies can be partially overcome through the strategic integration of archived samples. While technical shifts were evident in certain aspects of transcriptomic data, core gene expression patterns remained robust, and the functionality of essential transcription factors (TFs) and kinases remained unaffected. These findings underscore the potential of archived samples as valuable resources for multi-omics research, providing a broader landscape for investigating cancer biology and paving the way for more comprehensive insights into this intricate field.

cancer biology↗

bioRxiv2026

microRNAs bidirectionally regulate FUT1 to modulate α-1,2-fucosylation and cancer-associated biology

The -1,2-fucosyltransferase, FUT1, plays a central role in blood type determination, the establishment of the gut microbiota, and cancer progression. Using high-throughput analysis, we mapped the miRNA regulatory landscape of FUT1 and found that miRNAs bidirectionally regulate this enzyme. Validation of miRFluR assay results across multiple cell lines confirmed that both upregulatory and downregulatory miRNA interactions affect endogenous FUT1 and its enzymatic product, -1,2-fucosylation. Inhibitors of endogenous miRNA impacted both enzyme and glycan levels, underscoring the biological impact of bidirectional miRNA regulation. Upregulatory binding sites (miR-200c-5p and miR-361-3p) and the downregulatory binding site (miR-29c-5p) identified in this work both displayed non-canonical binding. Notably, miRNAs upregulating FUT1 are depleted in various cancers, aligning with observations that loss of -1,2-fucosylation is a hallmark of esophageal cancer, melanoma biogenesis, and metastasis. Furthermore, integration of our previous work with the current results indicates that the loss of miR-200c family has a strong correlation with the loss of -1,2-fucosylation during epithelial-to-mesenchymal transition. Together, these results identify miRNAs as key regulators of FUT1 and demonstrate that bidirectional miRNA control of glycosyltransferases can reshape cell-surface glycosylation with important implications for cancer biology.

molecular biology↗

bioRxiv2018

Constructing and visualizing cancer genomic maps in 3D spatial context by phenotype-based high-throughput laser-aided isolation and sequencing (PHLI-seq)

A spatially resolved analysis of the heterogeneous cancer genome, in which the data are connected to the three-dimensional space of a tumour, is crucial to understand cancer biology and the clinical impact of cancer heterogeneity on patients. However, despite recent progress in spatially resolved transcriptomics, spatial mapping of genomic data in a high-throughput and high-resolution manner has been challenging due to current technical limitations. Here, we describe a novel approach, phenotype-based high-throughput laser-aided isolation and sequencing (PHLI-seq), which enables high-throughput isolation of a single-cell or a small number of cells and their genome-wide sequence analysis to construct genomic maps within cancer tissue in relation to the phenotypes of the cells. By applying PHLI-seq, we reveal the heterogeneity of breast cancer tissues at a high resolution and map the genomic landscape of the cells to their corresponding spatial locations and phenotypes in the tumour mass. Additionally, with different staining modalities, the genotypes of the cells can be connected to corresponding phenotypic information of the tissue. Together with the spatially resolved genomic analysis, we can infer the histories of heterogeneous cancer cells in two or three dimensions, providing significant insight into cancer biology and precision medicine.

genomics↗

bioRxiv2022

Biologically relevant integration of transcriptomics profiles from cancer cell lines, patient-derived xenografts and clinical tumors using deep neural networks

Cell lines and patient-derived xenografts are essential to cancer research, however, the results derived from such models often lack clinical translatability, as these models do not fully recapitulate the complex cancer biology. It is critically important to better understand the systematic differences between cell lines, xenografts and clinical tumors, and to be able to identify pre-clinical models that sufficiently resemble the biological characteristics of clinical tumors across different cancers. On another side, direct comparison of transcriptional profiles from pre-clinical models and clinical tumors is infeasible due to the mixture of technical artifacts and inherent biological signals. To address these challenges, we developed MOBER, Multi-Origin Batch Effect Remover method, to simultaneously extract biologically meaningful embeddings and remove batch effects from transcriptomic datasets of different origin. MOBER consists of two neural networks: conditional variational autoencoder and source discriminator neural network that is trained in adversarial fashion. We applied MOBER on transcriptional profiles from 932 cancer cell lines, 434 patient-derived tumor xenografts and 11159 clinical tumors and identified pre-clinical models with greatest transcriptional fidelity to clinical tumors, and models that are transcriptionally unrepresentative of their respective clinical tumors. MOBER can conserve the biological signals from the original datasets, while generating embeddings that do not encode confounder information. In addition, it allows for transformation of transcriptional profiles of pre-clinical models to resemble the ones of clinical tumors, and therefore can be used to improve the clinical translation of insights gained from pre-clinical models. As a batch effect removal method, MOBER can be applied widely to transcriptomics datasets of different origin, allowing for integration of multiple datasets simultaneously.

bioinformatics↗

bioRxiv2018

PSMA Expression in the Hi-Myc Model; Extended Utility of a Representative Model of Prostate Cancer for Biological Insight and as a Drug Discovery Tool

Prostate specific membrane antigen (PSMA), also known as glutamate carboxypeptidase II (GCPII), is highly overexpressed in primary and metastatic prostate cancer (PCa). This has led to the development of radiopharmaceuticals for targeted imaging and therapy under current clinical evaluation. Despite this progress, the exact biological role of the protein in prostate cancer development and progression has not been fully elucidated. This is in part because the human PSMA and mouse PSMA (mPSMA) have different patterns of anatomical expression which confound study in the most widely utilized model organisms. Most notably, mPSMA is not expressed in the healthy murine prostate. Here, we reveal that mPSMA is highly upregulated in the prostate and prostate adenocarcinoma in the spontaneous Hi-Myc mouse model, a highly accurate and well characterized mouse model of prostate cancer development. Antibody detection and molecular imaging tools are used to confirm that mPSMA is expressed from early prostatic intraepithelial neoplasia (PIN) through adenocarcinoma.

cancer biology↗

bioRxiv2026

Dissecting context-dependent cancer vulnerabilities using Perturb-seq

Background CRISPR-mediated viability assays in diverse cancer cell lines have informed cancer biology and precision medicine, but cell fitness is not the only cancer-relevant phenotype. Gene expression profiling provides insight into cellular stress, inflammation, and differential state, while still identifying activation of cell-death pathways. Perturb-seq allows scalable functional genomics screening of expression phenotypes at single-cell resolution, however existing datasets cover only a small number of work-horse cell lines. Results We produced a proof-of-concept Perturb-seq dataset targeting 100 genes in 16 diverse cancer cell lines. In the process, we established methods to address single-cell technical artifacts, identified Cas9-mediated chromosomal aberrations and assessed screen quality. Even with a limited library, we observed common signatures of deleting essential genes as well as context-specific responses based on intrinsic genomic properties of the models. For example, we inferred a previously undescribed relationship between dependence on the ER-golgi transport gene immediate early response 3 interacting protein 1 (IER3IP1) and oxidative stress, demonstrating the potential of integrated Perturb-seq for hypothesis generation. Conclusions We established a framework for building a comprehensive map of post-perturbational transcriptional phenotypes using parallel Perturb-seq experiments across multiple cell lines. We demonstrated that integrated Perturb-seq experiments spanning diverse contexts enable hypotheses about gene function specific to tissue types or cancer subtypes - suggesting large-scale, genome-wide datasets would offer invaluable insight into the highly context-dependent nature of cancer biology.

cancer biology↗

bioRxiv2020

Transcriptomic and Proteomic analysis of clear cell foci (CCF) in the human non-cirrhotic liver identifies several differentially expressed genes and proteins with functions in cancer cell biology and glycogen metabolism.

Clear cell foci (CCF) of the liver are considered to be pre-neoplastic lesions of hepatocellular adenomas and carcinomas. They are hallmarked by glycogen-overload and activation of AKT/mTOR-signaling. Here we report the transcriptome and proteome of CCF extracted from human liver biopsies by laser capture microdissection. We mainly found genes and proteins with lower expression in CCF compared to control samples. Of 14 differentially expressed transcripts only 3 were overexpressed and belong to previously uncharacterized long non-coding RNAs. At the protein level, 3 proteins were at least 1.5-fold higher expressed in CCF than in control samples, while 22 proteins were at least 1.5-fold less abundant. Using immunohistochemistry, the reduced expressions of STBD1, USP28, monad/WDR92, CYB5B and HSPE1 were validated in CCF. Knockout of STBD1, the gene coding for Starch-binding domain-containing protein 1, in mice did not have an effect on liver glycogen levels. Similiarly, Usp28 knockout in mice did not affect glycogen storage in diethylnitrosamine-induced liver carcinoma. Thus, these data indicate that reduced expression of STBD1 or USP28 in CCF is unlikely to explain glycogen overload. Our study revealed several novel proteins and RNAs that are differentially expressed in CCF and have functions relevant for carcinogenesis or the glycogen metabolism.

cancer biology↗

bioRxiv2018

Cancer evolution in a changing microenvironment

One of the great unsolved puzzles in cancer biology is not why cancers occur, but rather, explaining why so few cancers occur compared with the theoretical number that could occur given the number of progenitor cells in the body and the normal mutation rate. We hypothesised that a contributory explanation is that the tumour microenvironment (TME) is not fixed, and that this could impair the ability of neoplastic cells to retain a high enough fitness to become a cancer. The TME has implicitly been assumed to be static in most cancer evolution models, and we therefore developed a mathematical model of spatial cancer evolution assuming that the TME, and thus the optimum cancer phenotype, change over time. Based on simulations, we show how cancer cell populations adapt to diverse changing TME conditions and fitness landscapes. Compared with static TMEs which generate neutral dynamics, changing TMEs lead to complex adaptations with spatio-temporal heterogeneity involving variable sub-clonal fitness, mixing, competition and phylogeny patterns. In many cases, cancer cell populations fail to grow or undergo spontaneous regression, and even extinction. Our analyses predict that cancer evolution in a changing TME is challenging, and can help to explain why cancer is neither inevitable nor as common as expected. Should cancer driver mutations with effects dependent of the TME exist, they are likely to be selected. Anti-cancer prevention and treatment strategies based on changing the TME are feasible and potentially effective.

cancer biology↗

bioRxiv2024

DriverOmicsNet: An Integrated Graph Convolutional Network for Multi-Omics Exploration of Cancer Driver Genes

BackgroundCancer is a complex and heterogeneous group of diseases driven by genetic mutations and molecular changes. Identifying and characterizing cancer driver genes (CDgs) is crucial for understanding cancer biology and guiding precision oncology. Integrating multi-omics data can reveal the intricate molecular interactions underlying cancer progression and treatment responses. MethodsWe developed a graph convolutional network (GCN) framework, DriverOmicsNet, that integrates multi-omics data using STRING protein-protein interaction (PPI) networks and correlation-based weighted correlation network analysis (WGCNA). We applied this framework to 15 cancer types, analyzing 5555 tumor samples to predict cancer-related features such as homologous recombination deficiency (HRD), cancer stemness, immune clusters, tumor stage, and survival outcomes. FindingsDriverOmicsNet demonstrated superior predictive accuracy and model performance metrics across all target labels when compared with GCN models based on STRING network alone. Gene expression emerged as the most significant feature, reflecting the dynamic and functional state of cancer cells. The combined use of STRING PPI and WGCNA networks enhanced the identification of key driver genes and their interactions. InterpretationOur study highlights the effectiveness of using GCNs to integrate multi-omics data for precision oncology. The integration of STRING PPI and WGCNA networks provides a comprehensive framework that improves predictive power and facilitates the understanding of cancer biology, paving the way for more tailored treatments.

cancer biology↗

bioRxiv2023

VLIB: Unveiling insights through Visual and Linguistic Integration of Biorxiv data relevant to cancer via Multimodal Large Language Model

The field of cancer research has greatly benefited from the wealth of new knowledge provided by research articles and preprints on platforms like Biorxiv. This study investigates the role of scientific figures and their accompanying captions in enhancing our comprehension of cancer. Leveraging the capabilities of Multimodal Large Language Models (MLLMs), we conduct a comprehensive analysis of both visual and linguistic data in biomedical literature. Our work introduces VLIB, a substantial scientific figure-caption dataset generated from cancer biology papers on Biorxiv. After thorough preprocessing, which includes figure-caption pair extraction, sub-figure identification, and text normalization, VLIB comprises over 500,000 figures from more than 70,000 papers, each accompanied by relevant captions. We fine-tune baseline MLLMs using our VLIB dataset for downstream vision-language tasks, such as image captioning and visual question answering (VQA), to assess their performance. Our experimental results underscore the vital role played by scientific figures, including molecular structures, histological images, and data visualizations, in conjunction with their captions, in facilitating knowledge translation through MLLMs. Specifically, we achieved a ROUGE score of 0.66 for VQA and 0.68 for image captioning, as well as a BLEU score of 0.72 for VQA and 0.70 for image captioning. Furthermore, our investigation highlights the potential of MLLMs to bridge the gap between artificial intelligence and domain experts in the field of cancer biology.

cancer biology↗

bioRxiv2025

The Second Mitochondrial Activator of Caspases (SMAC) regulates growth, inflammation and mitochondrial integrity in cancer cells

SMAC is a mitochondrial intermembrane space protein, which is released during apoptosis and whose known function is antagonism of inhibitor of apoptosis proteins in the cytosol, to facilitate caspase activation. Recent data suggest that SMAC can also be released by sub-lethal signals in the apoptosis pathway, in the absence of cell death. We here explored potential functions of SMAC in non-apoptotic cells. We found that a portion of SMAC is spontaneously released into the cytosol in the absence of apoptosis, regulated by the BCL-2-family proteins BAX and BAK and the fission GTPase DRP1. In cancer cell lines, SMAC was required for the activation of caspases in lethal and non-lethal conditions, while this contribution to caspase-activation was much smaller in non-malignant fibroblast lines. In cells with high levels of cytosolic SMAC, SMAC deficiency reduced in vitro migration, invasion and anchorage-independent growth. SMAC-deficient cells further showed a reduced activity in interferon signalling, associated with reduced cytosolic presence of mitochondrial DNA and activation of the stimulator of interferon genes (STING), and SMAC expression levels correlated with interferon-induced genes in cancer data sets. We further found that SMAC can regulate mitochondrial morphology and integrity. Finally, high gene-expression of SMAC was associated with poor prognosis in patients of several cancer types. These results identify SMAC as a regulator of inflammation and growth behaviour of cancer cells. They further report a mitochondrial function of SMAC and demonstrate a role of SMAC in human cancer biology across several cancer entities.

cell biology↗