Biology subjects

Munoz-Barrutia, A.

Publications and source records attributed to Munoz-Barrutia, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Quantitative Donor Histology and Transcriptomics Link Pancreatic Composition and Precancers Across the Adult Lifespan

Background & Aims: Pancreatic intraepithelial neoplasia (PanIN) is the primary precursor to invasive pancreatic ductal adenocarcinoma (PDAC). While PanIN is common in the general population, PDAC is rare. PDAC risk rises with age, diabetes mellitus, and obesity, but whether this increased risk reflects a greater precancer burden is unknown, as previous studies assessed PanIN presence and grade. We aimed to quantify pancreatic composition and PanIN burden across the adult lifespan to identify demographics of donors carrying disproportionate precancer burden. Methods: We applied CODAvision, a deep-learning segmentation pipeline, to 410 high-quality pancreas sections from Genotype-Tissue Expression (GTEx) donors aged 21 to 70 years. We screened twenty-seven donor phenotypes against 14 compositional measures, applying Benjamini-Hochberg correction for multiple testing. We further related tissue composition and donor phenotype to bulk gene expression in 281 donors to understand which demographics are associated with a compositional or transcriptional fingerprint. Results: PanIN was present in 83% of donors. Acinar proportion declined with age, offset by fat and stroma. Median PanIN burden did not change with age, though the fitted 90th percentile rose from 0.21% to 0.72% of exocrine tissue (uncorrected P=.002). The 39 sections (10%) with high-outlier PanIN burden came disproportionately from donors with type 2 diabetes mellitus and hypertension and had greater stromal proportion and higher fibroblast and immune-cell densities. Both cell types were denser in peri-PanIN stroma than in the surrounding pancreatic parenchyma. PanIN abundance was associated with a gastric transcriptional signature including VSIG1, TFF1, and MUC5AC. Conclusions: Most donors carried little PanIN at any age, whereas high burden was concentrated in a minority of donors more likely to have type 2 diabetes mellitus, hypertension, and inflamed, fibrotic stroma. Because PanIN was present in most donors, its presence did not distinguish high-risk groups, whereas quantitative burden did, concentrating on the metabolic phenotypes already under consideration for pancreatic surveillance.

bioinformatics↗

bioRxiv2026

FilAdh: degradation-aware analysis of focal adhesions and actin stress fibers

Focal adhesions (FAs) and actin stress fibers (SFs) are mechanically coupled but often analyzed separately. We present FilAdh, a unified framework for FA instance segmentation, SF instance reconstruction, morphometry, and FA-anchorage-based SF classification. Across 57 held-out HeLa, SUIT-2, and MRC-5 cell line images, FilAdh achieved the highest FA Dice, intersection over union (IoU), and recall and the highest SF clDice and skeleton precision among benchmarked methods; paired t tests with Holm correction indicated significant gains over all compared methods for FA Dice, IoU, and recall, and over classical methods for SF centerline metrics, with no significant differences detected relative to the deep-learning comparators. Conditional on expert-curated FA and SF masks, SFs were assigned to dorsal, ventral, and peripheral classes with 81.5%-87.3% accuracy. Controlled Poisson-Gaussian noise, Gaussian defocus, and point-spread-function blur revealed distinct, structure-specific errors in overlap and object-level measurements. By linking segmentation and reconstruction to count, area, length, apparent width, and structural class, FilAdh offers an interpretable view of the FA-actin structural system and makes degradation-dependent measurement biases explicit. BIGGER PICTURECells sense and respond to their physical environment through focal adhesions and actin stress fibers. The two form a mechanically coupled system, yet they are usually analyzed separately, with workflows re-tuned for each new set of images. FilAdh analyzes both within a single framework, tailoring segmentation, reconstruction, and classification to each structures geometry: compact adhesion patches versus thin, extended fiber networks. Testing on expert-annotated references and under controlled degradations that mimic low signal, focus errors, and optical blur shows that these common imaging imperfections distort adhesion and fiber measurements in different ways. The same design principle--matching the analysis to each structures shape and judging methods by how errors propagate into the final biological measurements--may benefit other bioimage-analysis problems. Released code, trained weights, and fixed data splits make all comparisons reproducible and extensible. HIGHLIGHTSO_LIFilAdh jointly quantifies focal adhesions and actin stress fibers C_LIO_LIFilAdh achieves the highest benchmarked FA overlap and comparable SF topology C_LIO_LIMatched synthetic degradations reveal geometry-specific measurement errors C_LIO_LIFA-SF spatial context enables dorsal, ventral, and peripheral SF classification C_LI

bioinformatics↗

bioRxiv2026

Transient cytoskeletal anisotropy encodes short-term mechanical memory

Cells can experience time-varying mechanical cues, particularly when navigating through changing and complex microenvironments. Yet whether and how cells retain and use a short-term mechanical memory of recent deformations remains unclear. Here we show that, in glioblastoma cells, this memory is encoded by transient cytoskeletal anisotropy. Using uniaxial magneto-mechanical actuation aligned or perpendicular to the cell long axis, nanoindentation, and selective cytoskeletal perturbations, we find that distinct architectures of the actin cytoskeleton drive opposite mechanical responses: actin stress fibers mediate stiffening under stretch, whereas the actin cortex underlies softening under perpendicular loading. Vimentin intermediate filaments are essential to stabilize actin organization under load, preserving deformation-specific mechanics. Quantitative imaging reveals that mechanical actuation induces network-specific alignment and anisotropy, stronger for actin than vimentin, that persists transiently after unloading and bias subsequent responses, revealing a short-lived, deformation-dependent mechanical memory. To integrate these observations, we develop a multi-network constitutive model that links cytoskeletal architecture and loading history to cell-scale mechanics, reproducing both the asymmetric mechanical responses and the measured reorganization dynamics. These findings provide a structural basis for short-term mechanical memory and suggest how cancer cells could exploit residual anisotropy to adapt to fluctuating solid stresses and confinement, transiently biasing polarization, force transmission, and directional persistence during invasion. They also identify vimentin-actin coupling and the kinetics of cytoskeletal remodeling as potential levers to limit the mechanical adaptability of invasive cancer cells.

biophysics↗

bioRxiv2026

High-fidelity bioimage restoration via adversarial learning

Live-cell microscopy restoration is constrained by a trade-off between inference latency and texture preservation. While diffusion models provide high textural fidelity, the computational cost of iterative sampling currently limits their use in low-latency instrument feedback loops. Here, we present NAFNet GAN, a restoration framework that couples an activation-free backbone with a perceptual adversarial objective to enable high-throughput analysis. Unlike diffusion architectures, NAFNet GAN achieves an inference latency of[~] 110 ms for 1024 x 1024 inputs, potentially suitable for real-time instrument feedback loops. Across eight datasets ranging from STED nanoscopy to histopathology, the method achieves the lowest Learned Perceptual Image Patch Similarity (LPIPS) scores in 7 of 8 benchmarks while preserving structural coherence (e.g., MS-SSIM > 0.968 in Cryo-EM), which facilitates reliable downstream analysis. Supported by performance benchmarks in the AI4Life Denoising Challenge, NAFNet GAN restores structural features from low-photon-budget acquisitions, maintaining the temporal resolution required for dynamic live-cell workflows.

bioengineering↗

bioRxiv2025

CODAvision: best practices and a user-friendly interface for rapid, customizable segmentation of medical images

Image-based machine learning tools have emerged as powerful resources for analyzing medical images, with deep learning-based semantic segmentation commonly utilized to enable spatial quantification of structures in images. However, customization and training of segmentation algorithms requires advanced programming skills and intricate workflows, limiting their accessibility to many investigators. Here, we present a protocol and software for automatic segmentation of medical images guided by a graphical user interface (GUI) using the CODAvision algorithm. This workflow simplifies the process of semantic segmentation of microanatomical structures by enabling users to train highly customizable deep learning models without extensive coding expertise. The protocol outlines best practices for creating robust training datasets, configuring model parameters, and optimizing performance across diverse biomedical image modalities. CODAvision enhances the usability of the CODA algorithm (Nature Methods, 2022) by streamlining parameter configuration, model training, and performance evaluation, automatically generating quantitative results and comprehensive reports. We expand beyond the original implementation of CODA to serial histology by demonstrating robust performance across numerous medical image modalities and diverse biological questions. We provide sample results in data types including histology, magnetic resonance imaging (MRI), and computed tomography (CT). We demonstrate the diverse use of this tool in applications including quantification of metastatic burden in in vivo models and deconvolution of spot-based spatial transcriptomics datasets. This protocol is designed for researchers with interest in rapid design of highly customizable semantic segmentation algorithms and a basic understanding of programming and anatomy.

bioinformatics↗

bioRxiv2025

Non-linear and time-domain sleep qEEG features predict CSF protein damage markers in early Alzheimers disease

Study ObjectivesIn preclinical Alzheimers disease (AD), oxidative stress plays a key role in the pathogenesis by promoting non-enzymatic protein modifications that lead to the accumulation of oxidized species, established biomarkers of oxidative damage detectable in cerebrospinal fluid (CSF). These molecular alterations contribute to impaired proteostasis and the dysfunction of sleep-regulating neural circuits, resulting in altered sleep electroencephalographic patterns. Due to the invasiveness of CSF sampling, quantitative electroencephalography (qEEG) is proposed as a non-invasive alternative for assessing oxidatively modified protein levels via Machine Learning (ML). MethodsForty-two mild-to-moderate AD patients underwent polysomnography (PSG). QEEG features were extracted. CSF protein oxidation markers levels --glutamic semialdehyde, aminoadipic semialdehyde, N{varepsilon}-carboxyethyl-lysine, N{varepsilon}-carboxymethyl-lysine, and N{varepsilon}-malondialdehyde-lysine --were assessed by gas chromatography/mass spectrometry, and ML models trained to predict CSF biomarker levels. Model generalizability was validated using EEG data from healthy controls. ResultsqEEG features from slow-wave sleep (SWS) and rapid eye movement (REM) sleep, particularly over frontal and central regions, yielded R2 > 0.625 for patients biomarker prediction. ConclusionqEEG is a non-invasive, scalable tool for detecting AD-related oxidative processes, with potential implications for early diagnosis and risk stratification. HighlightsO_LISleep EEG features predict CSF oxidative biomarkers in early Alzheimers disease. C_LIO_LIPeak-to-peak amplitude, variance, and entropy were the top predictive features. C_LIO_LINREM sleep, especially SWS, and REM sleep provided the most informative EEG signals. C_LIO_LIEEG-based models achieved accurate, non-invasive biomarker estimation. C_LIO_LISleep qEEG may enable early detection and risk stratification in Alzheimers. C_LI

neuroscience↗

bioRxiv2025

In-depth 3D Exploration of Autosomal Dominant Polycystic Kidney Disease Through Light Sheet Fluorescence Microscopy

Autosomal Dominant Polycystic Kidney Disease (ADPKD) is the most prevalent genetic kidney disorder. Animal preclinical studies are one of the main tools to study this disease, often through either 2D histology imaging for high-resolution analysis or CT or MRI for full kidney segmentation. As an alternative to these modalities, we propose the use of Light Sheet Fluorescence Microscopy (LSFM) for high-resolution 3D imaging of healthy and ADPKD-induced mouse kidneys, enabling a detailed volumetric morphological analysis of the diseases effects. In a mouse ADPKD model, ex vivo imaging of the kidneys was performed through LSFM, after which a combination of machine learning and other processing techniques allowed us to perform an in-depth image analysis. This includes the segmentation of key structures, such as the full kidney volume and, within it, its internal cavities, cortex, glomeruli, and cysts, complemented by texture analysis of tubular structures in the cortical area. Pathological kidneys exhibited significant volume enlargement and increased internal cavities due to cystogenesis. While glomerular count remained stable, their spatial distribution was altered, showing increased interglomerular distances and show-casing the deformations produced by the disease. The texture analysis of tubules from the cortex region identified Local Binary Pattern (LBP) uniformity and porosity as key biomarkers of tissue deformation, which could be used as markers to further evaluate the development of the disease. These findings underscore the potential of LSFM imaging as a powerful tool for detailed ADPKD characterization and treatment assessment.

bioengineering↗

bioRxiv2024

SAM-based Automatic Workflow for Histology Cyst Segmentation in Autosomal Dominant Polycystic Kidney Disease

Autosomal Dominant Polycystic Kidney Disease (ADPKD) is a genetic disorder characterized by the development of numerous cysts in the kidneys, ultimately leading to significant structural alterations and renal failure. Detailed investigations of this disease frequently utilize histological analyses of kidney sections across various stages of ADPKD progression. In this paper, we introduce an automated workflow leveraging the Segment Anything Model (SAM) neural network, complemented by a series of post-processing steps, to autonomously segment cysts in histological images. This approach eliminates the need for manual annotations or preliminary training phases and enables precise quantification of cystic changes over entire kidney sections. Application of this method to sequential histology images across the development timeline of ADPKD in mice demonstrated a notable increase in the proportion of diseased tissue from 8 to 12 weeks and from 12 to 16 weeks, with the cysts appearing progressively lighter. Our workflow not only surpasses the performance of the existing Cystanalyser tool but also offers enhanced flexibility and accuracy in full-image segmentation. The developed workflow is made publicly accessible to facilitate its adoption as an efficient tool for rapid and reliable cyst segmentation in histological studies.

bioinformatics↗

bioRxiv2024

3D histology reveals that immune response to pancreatic precancers is heterogeneous and depends on global pancreas structure

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer for which few effective therapies exist. Immunotherapies specifically are ineffective in pancreatic cancer, in part due to its unique stromal and immune microenvironment. Pancreatic intraepithelial neoplasia, or PanIN, is the main precursor lesion to PDAC. Recently it was discovered that PanINs are remarkably abundant in the grossly normal pancreas, suggesting that the vast majority will never progress to cancer. Here, through construction of 48 samples of cm3-sized human pancreas tissue, we profiled the immune microenvironment of 1,476 PanINs in 3D and at single-cell resolution to better understand the early evolution of the pancreatic tumor microenvironment and to determine how inflammation may play a role in cancer progression. We found that bulk pancreatic inflammation strongly correlates to PanIN cell fraction. We found that the immune response around PanINs is highly heterogeneous, with distinct immune hotspots and cold spots that appear and disappear in a span of tens of microns. Immune hotspots generally mark locations of higher grade of dysplasia or locations near acinar atrophy. The immune composition at these hotspots is dominated by naive, cytotoxic, and regulatory T cells, cancer associated fibroblasts, and tumor associated macrophages, with little similarity to the immune composition around less-inflamed PanINs. By mapping FOXP3+ cells in 3D, we found that regulatory T cells are present at higher density in larger PanIN lesions compared to smaller PanINs, suggesting that the early initiation of PanINs may not exhibit an immunosuppressive response. This analysis demonstrates that while PanINs are common in the pancreases of most individuals, inflammation may play a pivotal role, both at the bulk and the microscopic scale, in demarcating regions of significance in cancer progression.

cancer biology↗

bioRxiv2024

Generative interpolation and restoration of images using deep learning for improved 3D tissue mapping

The development of novel imaging platforms has improved our ability to collect and analyze large three-dimensional (3D) biological imaging datasets. Advances in computing have led to an ability to extract complex spatial information from these data, such as the composition, morphology, and interactions of multi-cellular structures, rare events, and integration of multi-modal features combining anatomical, molecular, and transcriptomic (among other) information. Yet, the accuracy of these quantitative results is intrinsically limited by the quality of the input images, which can contain missing or damaged regions, or can be of poor resolution due to mechanical, temporal, or financial constraints. In applications ranging from intact imaging (e.g. light-sheet microscopy and magnetic resonance imaging) to sectioning based platforms (e.g. serial histology and serial section transmission electron microscopy), the quality and resolution of imaging data has become paramount. Here, we address these challenges by leveraging frame interpolation for large image motion (FILM), a generative AI model originally developed for temporal interpolation, for spatial interpolation of a range of 3D image types. Comparative analysis demonstrates the superiority of FILM over traditional linear interpolation to produce functional synthetic images, due to its ability to better preserve biological information including microanatomical features and cell counts, as well as image quality, such as contrast, variance, and luminance. FILM repairs tissue damages in images and reduces stitching artifacts. We show that FILM can decrease imaging time by synthesizing skipped images. We demonstrate the versatility of our method with a wide range of imaging modalities (histology, tissue-clearing/light-sheet microscopy, magnetic resonance imaging, serial section transmission electron microscopy), species (human, mouse), healthy and diseased tissues (pancreas, lung, brain), staining techniques (IHC, H&E), and pixel resolutions (8 nm, 2 {micro}m, 1mm). Overall, we demonstrate the potential of generative AI in improving the resolution, throughput, and quality of biological image datasets, enabling improved 3D imaging.

bioinformatics↗

bioRxiv2024

BiaPy: A unified framework for versatile bioimage analysis with deep learning

BiaPy is an open-source library and application that streamlines the use of common deep learning approaches for bioimage analysis. Designed to simplify technical complexities, it offers an intuitive interface, zero-code notebooks, and Docker integration, catering to both users and developers. While focused on deep learning workflows for 2D and 3D image data, it enhances performance with multi-GPU capabilities, memory optimization, and scalability for large datasets. Although BiaPy does not encompass all aspects of bioimage analysis, such as visualization and manual annotation tools, it empowers researchers by providing a ready-to-use environment with customizable templates that facilitate sophisticated bioimage analysis workflows.

bioinformatics↗

bioRxiv2024

Gut Analysis Toolbox: Automating quantitative analysis of enteric neurons

The enteric nervous system (ENS) plays an important role in coordinating gut function. The ENS consists of an extensive network of neurons and glial cells within the wall of the gastrointestinal tract. Alterations in neuronal distribution, function, and type are strongly associated with enteric neuropathies and gastrointestinal (GI) dysfunction and can serve as biomarkers for disease. However, current methods for assessing neuronal counts and distribution suffer from undersampling. This is partly due to challenges associated with imaging and analyzing large tissue areas, and operator bias due to manual analysis. Here, we present the Gut Analysis Toolbox (GAT), an image analysis tool designed for characterization of enteric neurons and their neurochemical coding using 2D images of GI wholemount preparations. GAT is developed for the Fiji distribution of ImageJ. It has a user-friendly interface and offers rapid and accurate cell segmentation. Custom deep learning (DL) based cell segmentation models were developed using StarDist. GAT also includes a ganglion segmentation model which was developed using deepImageJ. In addition, GAT allows importing of segmentation generated by other software. DL models have been trained using ZeroCostDL4Mic on diverse datasets sourced from different laboratories. This captures the variability associated with differences in animal species, image acquisition parameters, and sample preparation across research groups. We demonstrate the robustness of the cell segmentation DL models by comparing them against the state-of-the-art cell segmentation software, Cellpose. To quantify neuronal distribution GAT applies proximal neighbor-based spatial analysis. We demonstrate how the proximal neighbor analysis can reveal differences in cellular distribution across gut regions using a published dataset. In summary, GAT provides an easy-to-use toolbox to streamline routine image analysis tasks in ENS research. GAT enhances throughput allowing unbiased analysis of larger tissue areas, multiple neuronal markers and numerous samples rapidly.

neuroscience↗

bioRxiv2024

Bridging the Gap: Integrating Cutting-edge Techniques into Biological Imaging with deepImageJ

This manuscript showcases the latest advancements in deepImageJ, a pivotal Fiji/ImageJ plugin for bioimage analysis in the life sciences. The plugin, known for its user-friendly interface, facilitates the application of diverse pre-trained neural networks to custom data. The manuscript demonstrates a number of deepImageJ capabilities, particularly in executing complex pipelines, 3D analysis, and processing large images. A key development is the integration of the Java Deep Learning Library (JDLL), expanding deepImageJs compatibility with various deep learning frameworks, including TensorFlow, PyTorch, and ONNX. This allows for running multiple engines within a single Fiji/ImageJ instance, streamlining complex bioimage analysis tasks. The manuscript details three case studies to demonstrate these capabilities. The first explores integrated image-to image translation and nuclei segmentation. The second focuses on 3D nuclei segmentation. The third case study deals with large image segmentation. These studies underscore deepImageJs versatility and power in bioimage analysis, emphasizing its role as a critical tool for life scientists and researchers. The advancements in deepImageJ bridge the gap between deep learning model developers and end-users, enabling a more accessible and efficient approach to biological image analysis. The advancements in deepImageJ, detailed in this paper, represent a significant leap in bioimage analysis, crucial for life sciences. By enhancing this Fiji/ImageJ plugin, the research bridges the gap between complex deep learning models and practical applications, making advanced bioimage analysis accessible to a broader audience. This integration of the Java Deep Learning Library (JDLL) within deepImageJ is particularly noteworthy, as it expands compatibility with leading deep learning frameworks. This allows for the seamless execution of multiple models in a single instance, simplifying the construction of complex image analysis pipelines. The implications of this research are far-reaching, extending beyond academic circles to potentially impact various sectors, including healthcare, pharmaceuticals, and biotechnology. The enhanced capabilities of deepImageJ in handling intricate pipelines, 3D analysis, and large images facilitate detailed and efficient analysis of biological data. Such advancements are vital for accelerating research and development in medical imaging, drug discovery, and understanding complex biological processes. This manuscript contribution to the field of bioimage analysis is significant, offering a tool that empowers researchers, irrespective of their computational expertise, to leverage advanced technologies in their work. The wide applicability and ease of use of deepImageJ have the potential to foster interdisciplinary collaborations, drive innovation, and facilitate discoveries across various scientific and industrial sectors.

bioinformatics↗

bioRxiv2023

Unraveling the Power of NAP-CNB's Machine Learning-enhanced Tumor Neoantigen Prediction

In this study, we present a proof-of-concept classical vaccination experiment that validates the in silico identification of tumor neoantigens (TNAs) using a machine learning-based platform called NAP-CNB. Unlike other TNA predictors, NAP-CNB leverages RNAseq data to consider the relative expression of neoantigens in tumors. Our experiments show the efficacy of NAP-CNB. Predicted TNAs elicited potent antitumor responses in vivo following classical vaccination protocols. Notably, optimal antitumor activity was observed when targeting the antigen with higher expression in the tumor, which was not the most immunogenic. Additionally, the vaccination combining different neoantigens resulted in vastly improved responses compared to each one individually, showing the worth of multiantigen-based approaches. These findings validate NAP-CNB as an innovative TNA-identification platform and make a substantial contribution to advancing the next generation of personalized immunotherapies

immunology↗

bioRxiv2023

Small extracellular vesicles promote stiffness-mediated metastasis

Tissue stiffness is a critical prognostic factor in breast cancer and is associated with metastatic progression. Here we show an alternative and complementary hypothesis of tumor progression whereby physiological matrix stiffness affects the quantity and protein cargo of small EVs produced by cancer cells, which in turn drive their metastasis. Primary patient breast tissue produces significantly more EVs from stiff tumor tissue than soft tumor adjacent tissue. EVs released by cancer cells on matrices that model human breast tumors (25 kPa; stiff EVs) feature increased adhesion molecule presentation (ITG2{beta}1, ITG6{beta}4, ITG6{beta}1, CD44) compared to EVs from softer normal tissue (0.5 kPa; soft EVs), which facilitates their binding to extracellular matrix (ECM) protein collagen IV, and a 3-fold increase in homing ability to distant organs in mice. In a zebrafish xenograft model, stiff EVs aid cancer cell dissemination through enhanced chemotaxis. Moreover, normal, resident lung fibroblasts treated with stiff and soft EVs change their gene expression profiles to adopt a cancer associated fibroblast (CAF) phenotype. These findings show that EV quantity, cargo, and function depend heavily on the mechanical properties of the extracellular microenvironment. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/545937v3_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@1be1dbdorg.highwire.dtl.DTLVardef@928710org.highwire.dtl.DTLVardef@1e14e9borg.highwire.dtl.DTLVardef@efdb7f_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2023

Automatic Classification of Normal and Abnormal Cell Division Using Deep Learning Analysis of Mitosis Videos

In recent years, there has been a surge in the development of methods for cell segmentation and tracking, with initiatives such as the Cell Tracking Challenge driving progress in the field. Most studies focus on regular cell population videos in which cells are segmented, cell tracks followed, and parental relationships annotated. However, DNA damage induced by genotoxic drugs or ionizing radiation provide additional abnormal cellular events of interest since they lead to aberrant behaviors such as abnormal cell divisions (i.e., resulting in a number of daughter cells different from two) and cell death. The dynamic development of those abnormal events can be followed using time lapse microscopy to be further analyzed. With this in mind, we developed an automatic mitosis classifier that categorizes small mitosis image sequences centered around a single cell as "Normal" or "Abnormal." These mitosis sequences were extracted from videos of cell populations exposed to varying levels of radiation that affect the cell cycles development. Such an approach can aid in detecting, tracking, and characterizing the behavior of the entire population. In this study, we explored several deep-learning architectures for working with 12-frame mitosis sequences. We found that a network with a ResNet50 backbone, modified to operate independently on each video frame and then combined using a Long Short-Term Memory (LSTM) layer, produced the best results in the classification (mean F1-score: 0.93 {+/-} 0.06). In future work, we plan to integrate the mitosis classifier in a cell segmentation and tracking pipeline to build phylogenetic trees of the entire cell population after genomic stress. Author SummaryIn recent years, there has been a growing interest in developing methods to analyze videos of cell populations, which show how cells move and divide over time. Typically, researchers focus on developing methods to automatically identify and track individual cells and their divisions. However, exposure to anticancer drugs or radiation can cause uncommon behaviors, such as abnormal cell divisions, which are of interest to experts studying the effects of these agents on cell behavior. To address this issue, we developed an automated tool that can determine whether a specific cell division seen in a video is normal or abnormal. We used video microscopy to capture small sequences of cell division, and then trained a deep-learning model to classify these sequences as either normal or abnormal. We found that our model achieved a high level of accuracy in this task. Our tool has the potential to aid experts in identifying abnormal cellular events, providing insights into the effects of genotoxic agents on cell behavior. In future work, we plan to integrate our tool into more complex methods for analyzing cell population videos, which may help us better understand the impact of toxic agents on the behavior of the entire cell population.

bioinformatics↗

bioRxiv2022

Tumor proliferation and invasion are coupled through cell-extracellular matrix friction

Cell proliferation and invasion are two key drivers of tumor progression and are traditionally considered two independent cellular processes regulated by distinct pathways. Through in vitro and in silico methods, we provide evidence that these two processes are intrinsically coupled through matrix-adhesion friction. Using novel tumor spheroids, we show that both tumor cell proliferation and invasion are limited by a volumetric carrying capacity of the system, i.e. maximum spatial cell concentration supported by the systems total cell count, nutrient consumption rate, and collagen gel mechanical properties. To manipulate these phenotypes in breast cancer cells, we modulate the expression of E-cadherin and its associated role in adhesion, invasion, and proliferation. We integrate these results into a mixed-constitutive formulation to computationally delineate the contributions of cellular and extracellular adhesion, stiffness, and mechanical properties of the extracellular matrix (ECM) to the proliferative and invasive fates of breast cancer tumor spheroids. Both approaches conclude that the dominant drivers of tumor fate are system properties modulating cell-ECM friction, such as E-cadherin dependent cell-ECM adhesion and matrix pore size.

biophysics↗

bioRxiv2022

MLL1 regulates cytokine-driven cell migration and metastasis

Cell migration is a critical requirement for cancer metastasis. Cytokine production and its role in cancer cell migration have been traditionally associated with immune cells in the tumor microenvironment. MLL1 is a histone methyltransferase that controls 3D cell migration via the secretion of cytokines, IL-6 and TGF-{beta}1, by the cancer cells themselves. In vivo, MLL1 depletion reduced metastatic burden and prolonged survival. MLL1 exerts its effects with its scaffold protein, Menin. Mechanistically, the MLL1-Menin interaction controls actin filament assembly via the IL-6/pSTAT3/Arp3 axis and acto-myosin contractility via the TGF-{beta}1/Gli2/ROCK1/2/pMLC2 axis, which regulate dynamic protrusion generation and 3D cell migration. MLL1 also regulates cell proliferation via mitosis-based and cell cycle-related pathways. Combining an MLL1-Menin inhibitor with Paclitaxel, a standard chemotherapeutic, abrogated tumor growth and metastasis in a syngeneic model. These results highlight the potential of targeting the MLL1 in metastasis prevention and its potential to be combined with currently administered chemotherapeutics. Statement of SignificanceWe identify MLL1 as being vital to metastasis, which causes the vast majority of cancer-related deaths. MLL1 controls cell migration, a requirement for metastasis, by regulating the secretion of cytokines. MLL1 inhibition lowers metastatic burden independent of its impact on primary tumor growth, highlighting its anti-metastatic potential in TNBC.

cancer biology↗