Biology subjects

Reyes-Aldasoro, C. C.

Publications and source records attributed to Reyes-Aldasoro, C. C..

18 recordsLinked to original sources

bioRxiv2026

How Mitochondria Distribute and Align in HeLa Cells: A Computational Analysis On Electron Microscopy Images.

This paper investigates the way in which mitochondria distribute and align inside HeLa cells observed with serial block-face scanning electron microscopy. Four models of alignment were considered: (1) mitochondria exhibiting no discernible alignment pattern, (2) mitochondria aligned pointing towards the nucleus of the cell, (3) mitochondria aligned all in one direction when viewed from above, (4) mitochondria aligned tangent to the surface of the nucleus. These models were named (1) unaligned, (2) petals, (3) racecars, and (4) clouds. The mitochondria, nucleus and plasma membrane of 25 individual cells were segmented. A total of 12,299 mitochondria were identified and analysed. Alignment of the major axis of each mitochondrion was calculated in two ways: relative to a ray that joins it to the centroid of the nucleus, and relative to a ray that joins it to the nucleus surface. Results indicate that mitochondria tend to align tangentially to the nucleus surface, i.e., a clouds model. In addition, differences in the spatial distributions of the mitochondria were found and quantified with clearly defined metrics. The methodology here presented can be extended to other acquisition settings where the distribution and alignment of cells could be important, for instance, histopathology.

bioinformatics↗

bioRxiv2025

RAS-PI3K Pathway in CAFs Shapes Physicochemical Properties of Tumor ECM and restrains Tumor progression

Cancer-associated fibroblasts display extensive functional plasticity, yet the signaling mechanisms that stabilize distinct CAF states remain poorly understood. Here, we identify stromal RAS-PI3K signaling as a key regulator of myofibroblastic CAF identity and matrix competence in lung cancer. Genetic disruption of the RAS-PI3K interaction in fibroblasts preserved upstream responsiveness to activating cues but impaired cytoskeletal remodeling, contractile execution and acquisition of a matrix-remodeling CAF program. As a result, RAS-PI3K-deficient CAFs generated extracellular matrices with defective collagen and fibronectin organization, reduced stiffness, altered microstructural properties and broad compositional remodeling of the matrisome, including changes in ECM glycoproteins and glycosylation-associated enzymes. These altered matrices failed to efficiently provide directional, adhesive and proliferative cues to tumor cells, attenuated EMT-associated programs and increased sensitivity to cisplatin plus pemetrexed. In vivo, fibroblast-specific RAS-PI3K disruption reduced tumor burden, decreased tumor cell proliferation, prolonged survival and improved chemotherapy response. Loss of RAS-PI3K signaling did not simply suppress CAF activation, but redirected CAFs toward a STAT3/NF-{kappa}B-linked immunomodulatory state associated with IL6, CXCL12 and PDGFR induction. Functionally, this altered stromal state promoted iNOS-associated macrophage features and was accompanied in vivo by reduced macrophage accumulation and increased CD8 T cell infiltration. These findings establish RAS-PI3K signaling as a stromal state-control mechanism that couples myofibroblastic matrix competence to tumor-supportive microenvironmental function while restraining alternative immunomodulatory CAF programs.

cancer biology↗

bioRxiv2024

Fibre tracing in biomedical images: An objective comparison between seven algorithms

Obtaining the traces and the characteristics of elongated structures is an important task in computer vision pipelines. In biomedical applications, the analysis of traces of vasculature, nerves or fibres of the extracellular matrix can help characterise processes like angiogenesis or the effect of a certain treatment. This paper presents an objective comparison of six existing methodologies (Edge detection, CT Fire, Scale Space, Twombli, U-Net and Graph Based) and one new approach called Trace Ridges to trace biomedical images with fibre-like structures. Trace Ridges is a fully automatic and fast algorithm that combines a series of image-processing algorithms including filtering, watershed transform and edge detection to obtain an accurate delineation of the fibre-like structures in a rapid time. To compare the algorithms, four biomedical data sets with very different characteristics were selected. Ground truth was obtained by manual delineation of the fibre-like structures. Three pre-processing filtering options were used as a first step: no filtering, Gaussian low-pass and DnCnn, a deep-learning filtering. Three distance error metrics (total, average and maximum distance from the obtained traces to the ground truth) and processing time were calculated. It was observed that no single algorithm outperformed the others in all metrics. For the total distance error, which was considered the most significative, Trace Ridges ranked first, followed by Graph Based, U-Net, Twombli, Scale Space, CT Fire and Edge Detection. In terms of speed, Trace Ridges ranked second only slightly slower than Edge Detection. Code is freely available at github.com/youssefarafat/Trace Ridges.

cancer biology↗

bioRxiv2024

Pulses of Class I PI3kinase activity identify the release and recapture of prey from neutrophil phagosomes

Class I PI3kinases coordinate the delivery of microbicidal effectors to the phagosome by forming the phosphoinositide lipid second messenger, phosphatidylinositol (3, 4, 5)-trisphosphate (PIP3). However, the dynamics of PIP3 in neutrophils during a bacterial infection are unknown. We have therefore developed an in vivo, live zebrafish infection model that enables visualisation of dynamic changes in Class 1 PI3kinases (PI3K) signalling on neutrophil phagosomes in real-time. We have identified that on approximately 12% of neutrophil phagosomes PHAkt-eGFP, a reporter for Class 1 PI3K signalling, re-recruits in pulsatile bursts. This phenomenon occurred on phagosomes containing structurally and morphologically distinct prey, including Staphylococcus aureus and Mycobacterium abscessus, and was dependent on the activity of the Class 1 PI3K isoform, PI3kinase {gamma}. Detailed imaging suggested that pulsing phagosomes represent neutrophils transiently reopening and reclosing phagosomes. This finding challenges the concept that phagosomes remain closed after prey engulfment and we propose that neutrophils occasionally use this alternative pathway of phagosome maturation to release phagosome contents and/or to restart phagosome maturation if digestion has stalled.

cell biology↗

bioRxiv2023

Relationship Between Irregularities of the Nuclear Envelope and Mitochondria in HeLa cells Observed with Electron Microscopy

This paper describes a methodology to analyse the complexity of HeLa cells as observed with electron microscopy, in particular the relationship between mitochondria and the roughness of the nuclear envelope as reflected by the invaginations of the surface. For this purpose, several segmentation mitochondria algorithms were quantitatively compared, namely: Topology, Image Processing, Topology and Image Processing, and Deep Learning, which provided the highest accuracy. The invaginations were successfully segmented with one image processing algorithm. Metrics were extracted for both structures and correlations between the mitochondria and invaginations were explored for 25 segmented cells. It was found that there was a positive correlation between the volume of invaginations and the volume of mitochondria, and negative correlations between the number and the mean volume of mitochondria, and between the volume of the cytoplasm and the aspect ratio of mitochondria. These results suggest that there is a relationship between the shape of a cell, its nucleus and its mitochondria; as well as a relationship between the number of mitochondria and their shapes. Whilst these results were obtained from a single cell line and a relatively small number of cells, they encourage further study as the methodology proposed can be easily applied to other cells and settings. Code and data are freely available. HeLa images are available from http://dx.doi.org/10.6019/EMPIAR-10094, code from https://github.com/reyesaldasoro/MitoEM, and segmented nuclei, cells, invaginations and mitochondria from https://github.com/reyesaldasoro/HeLa_Cell_Data.

bioinformatics↗

bioRxiv2022

Evaluation of training data, ground truth and shape variability in the semantic segmentation of HeLa cells observed with electron microscopy

This paper describes the quantitative and qualitative evaluation of training data, ground truth and the variability of the shape of HeLa cells as observed with electron microscopy in the process of semantic segmentation. HeLa cells are a widely used immortal cell line, but the principles described here could apply to other cells. From a data set of 8, 000x8, 000x517 voxels, a region of interest (ROI) of 2, 000x2, 000x300 was cropped for quantitative evaluation with the qualitative evaluation performed on a larger section of interest. Patches of data and labels were used to train a U-Net architecture from scratch, which was compared also with a traditional image processing algorithm. The correctness of ground truth, that is, the inclusion of one or more nuclei within a region of interest was evaluated with a hand-drawn ground truth of the ROI. The impact of the extent of training data was evaluated by comparing results from 36,000 data and label patches extracted from the odd slices in the central region, to 135,000 patches obtained from every other slice in the set. Accuracy and Jaccard similarity index were higher for the larger training set with multiple nuclei in the ground truth. However, when the U-Nets were used to segment the section of interest beyond the ROI, results were not as good as expected, suggesting that the very complicated nature of the shapes of HeLa cells cannot be learned from a single cell. Data, code and ground truth have been made publicly released through Empair, GitHub and Zenodo.

cancer biology↗

bioRxiv2021

Volumetric Semantic Instance Segmentation of the Plasma Membrane of HeLa Cells

In this work, the unsupervised volumetric semantic segmentation of the plasma membrane of HeLa cells as observed with Serial Block Face Scanning Electron Microscopy is described. The resin background of the images was segmented at different slices of a 3D stack of 518 slices with 8, 192 x 8, 192 pixels each. The background was used to create a distance map which helped identify and rank the cells by their size at each slice. The centroids of the cells detected at different slices were linked to identify them as a single cell that spanned a number of slices. A subset of these cells, i.e., largest ones and those not close to the edges were selected for further processing. The selected cells were then automatically cropped to smaller regions of interest of 2, 000 x 2, 000 x 300 voxels that were treated as cell instances. Then, for each of these volumes the nucleus was segmented and the cell was separated from any neighbouring cells through a series of traditional image processing steps that followed the plasma membrane. The segmentation process was repeated for all the regions selected. For one cell for which the ground truth was available, the algorithm provided excellent results in Accuracy (AC) and Jaccard Index (JI): Nucleus: JI = 0.9665, AC= 0.9975, Cell and Nucleus JI = 0.8711, AC = 0.9655, Cell only JI = 0.8094, AC = 0.9629. A limitation of the algorithm for the plasma membrane segmentation was the presence of background, as in cases of tightly packed cells. When tested for these conditions, the segmentation of the nuclear envelope was still possible. All the code and data are released openly through GitHub, Zenodo and EMPIAR.

cell biology↗

bioRxiv2020

Automatic Analysis of Bees' Waggle Dance

This work describe an algorithm for the automatic analysis of the waggle dance of honeybees. The algorithm analyses a video of a beehive with 13,624 frames, acquired at 25 frames/second. The algorithm employs the following traditional image processing steps: conversion to grayscale, low pass filtering, background subtraction, thresholding, tracking and clustering to detect run of bees that perform waggle dances. The algorithm detected 44,530 waggle events, i.e. one bee waggling in one time frame, which were then clustered into 511 waggle runs. Most of these were concentrated in one section of the hive. The accuracy of the tracking was 90% and a series of metrics like intra-dance variation in angle and duration were found to be consistent with literature. Whilst this algorithm was tested on a single video, the ideas and steps, which are simple as compared with Machine and Deep Learning techniques, should be attractive for researchers in this field who are not specialists in more complex techniques.

animal behavior and cognition↗

bioRxiv2020

Estimation of cellularity in tumours treated with Neoadjuvant therapy: A comparison of Machine Learning algorithms

This paper describes a method for residual tumour cellularity (TC) estimation in Neoadjuvant treatment (NAT) of advanced breast cancer. This is determined manually by visual inspection by a radiologist, then an automated computation will contribute to reduce time workload and increase precision and accuracy. TC is estimated as the ratio of tumour area by total image area estimated after the NAT. The method proposed computes TC by using machine learning techniques trained with information on morphological parameters of segmented nuclei in order to classify regions of the image as tumour or normal. The data is provided by the 2019 SPIE Breast challenge, which was proposed to develop automated TC computation algorithms. Three algorithms were implemented: Support Vector Machines, Nearest K-means and Adaptive Boosting (AdaBoost) decision trees. Performance based on accuracy is compared and evaluated and the best result was obtained with Support Vector Machines. Results obtained by the methods implemented were submitted during ongoing challenge with a maximum of 0.76 of prediction probability of success.

pathology↗

bioRxiv2020

Morphological estimation of Cellularity on Neo-adjuvant treated breast cancer histological images

This paper describes a methodology that extracts morphological features from histological breast cancer images stained for Hematoxilyn and Eosin (H&E). Cellularity was estimated and the correlation between features and the residual tumour size cellularity after a Neo-Adjuvant treatment (NAT) was examined. Images from whole slide imaging (WSI) were processed automatically with traditional computer vision methods to extract twenty two morphological parameters from the nuclei, epithelial region and the global image. The methodology was applied to a set of images from breast cancer under NAT. The data came from the BreastPathQ Cancer Cellularity Challenge 2019, and consisted of 2579 patches of 255x255 pixels of H&E histopatological samples from NAT treatment patients. The methodology automatically implements colour separation, segmentation and morphological analysis using traditional algorithms (K-means grouping, watershed segmentation, Otsus binarisation). Linear regression methods were applied to determine strongest correlation between the parameters and the cancer cellularity. The morphological parameters showed correlation with the residual tumour cancer cellularity. The strongest correlations corresponded to the stroma concentration value (r = -0.9786) and value from HSV image colour space (r = -0.9728), both from a global image parameters.

pathology↗

bioRxiv2020

Geometric differences between nuclear envelopes of Wild-type and Chlamydia trachomatis-infected HeLa cells

In this work, the geometrical characteristics of two different types of cells observed with Electron Microscopy were analysed. The nuclear envelope of Wild-type HeLa cells and Chlamydia trachomatis-infected HeLa cells were automatically segmented and then modelled against a spheroid and converted to a two-dimensional surface. Geometric measurements from this surface and the volumetric nuclear envelope were extracted to compare the two types of cells. The measurements included the nuclear volume, the sphericity of the nucleus, its flatness or spikiness. In total 13 different cells were segmented: 7 Wild-type and 6 Chlamydia trachomatis-infected. The cells were statistically different in the following measurements. Wild-type HeLa cells have greater volumes than that of Chlamydia trachomatis-infected HeLa cells and they are more spherical as Jaccard index suggests. Standard deviation ({sigma}), and range of values for the nuclear envelope, which shows the distance of the highest peaks and deepest valleys from the spheroid, were also extracted from the modelling against a spheroid and these metrics were used to compare two different data sets in order to draw conclusions.

cell biology↗

bioRxiv2020

Optimisation strategies for the registration of Computed Tomography images of electropalatography

Electropalatography is a technique that employs a custom-made artificial palate to measure the contact established between the tongue and the hard palate. This technique is widely used in treatment of articulation disorders and studies of speech. In order to evaluate the accuracy of the electropalate, the device needs to be separated from the volume that usually contains electropalate worn on hard palate. This is done with the use of segmentation techniques. Prior to the segmentation, the registration of the two volumes, one containing the electroplate worn on hard palate, and one containing only hard palate, needs to be done. The registration is a technique of aligning multiple images by geometrical transform. Over the years, many methods for registration have been developed. The following paper describes the method of registration based on sensitivity analysis. Sensitivity analysis is a technique that evaluates the change in the number of pixels with different intensity with a shift of the volumes in different dimensions. Then based on the found optimal shift value, the shift in different dimension of the matrix is made. The technique successfully improves the alignments between two data sets, reducing the number of non-matching pixels. The sensitivity analysis-based registration should be useful in the future improvement of image processing tools that are crucial for the medical imaging.

bioinformatics↗

bioRxiv2020

Comparative study of contact repulsion in control and mutant macrophages using a novel interaction detection

This paper compares the contact-repulsion movement of mutant and wild-type macrophages using a novel interaction detection mechanism. The migrating macrophages are observed in Drosophila embryos. The study is carried out by a framework called macrosight, which analyses the movement and interaction of migrating macrophages. The framework incorporates a segmentation and tracking algorithm into analysing motion characteristics of cells after contact. In this particular study, the interactions between cells is characterised in the case of control embryos and Shot3 mutants, where the cells have been altered to suppress a specific protein, looking to understand what drives the movement. Statistical significance between control and mutant cells was found when comparing the direction of motion after contact in specific conditions. Such discoveries provide insights for future developments in combining biological experiments to computational analysis. Cell Segmentation, Cell Tracking, Macrophages, Cell Shape, Contact Analysis

cell biology↗

bioRxiv2020

Semantic Segmentation of HeLa Cells: An Objective Comparison between one Traditional Algorithm and Three Deep-Learning Architectures

In this work, images of a HeLa cancer cell were semantically segmented with one traditional image-processing algorithm and three deep learning architectures: VGG16, ResNet18 and Inception-ResNet-v2. Three hundred slices, each 2000 x 2000 pixels, of a HeLa Cell were acquired with Serial Block Face Scanning Electron Microscopy. The deep learning architectures were pre-trained with ImageNet and then fine-tuned with transfer learning. The image-processing algorithm followed a pipeline of several traditional steps like edge detection, dilation and morphological operators. The algorithms were compared by measuring pixel-based segmentation accuracy and Jaccard index against a labelled ground truth. The results indicated a superior performance of the traditional algorithm (Accuracy = 99%, Jaccard = 93%) over the deep learning architectures: VGG16 (93%, 90%), ResNet18 (94%, 88%), Inception-ResNet-v2 (94%, 89%).

cancer biology↗

bioRxiv2020

A machine learning approach for Colles' fracture treatment diagnosis

Wrist fractures (e.g. Colles fracture) are the most common injuries in the upper extremity treated in Emergency Departments. Most patients are treated with a procedure called Manipulation under Anaesthesia. Surgical treatment may still be needed in complex fractures or if the wrist stability is not restored. This can lead to inefficiency in constrained medical resources and patients inconvenience. Previous geometric measurements in X-ray images were found to provide statistical differences between healthy controls and fractured cases as well as pre- and post-intervention images. The most discriminating measurements were associated with the texture analysis of the radial bone. This work presents further analysis of these measurements and applying them as features to identify the best machine learning model for Colles fracture treatment diagnosis. Random forest was evaluated to be the best model based on validation accuracy. The non-linearity of the measurement features has attributed to the superior performance of an ensembled tree-based model. It is also interesting that the most important features (i.e. texture and swelling) required in the optimised random forest model are consistent with previous findings.

bioinformatics↗

bioRxiv2019

Cell Tracking Profiler: a user-driven analysis framework for evaluating 4D live cell imaging data

Accurate measurements of cell morphology and behaviour are fundamentally important for understanding how disease, molecules and drugs affect cell function in vivo. Using muscle stem cell (muSC) responses to injury in zebrafish as our biological paradigm we have established a ground truth for muSC cell behaviour. This revealed that variability in segmentation and tracking algorithms from commonly used programs are error-prone, leading us to develop a fast semi-automated image analysis pipeline that allows user defined parameters for segmentation and correction of cell tracking. Cell Tracking Profiler (CTP) operates through the freely available Icy platform, and allows user-managed cell tracking from 3D time-lapsed datasets to provide measures of cell shape and movement. Using dimensionality reduction methods, multiple correlation and regression analyses we identify myosin II-dependent parameters of muSC behaviour during regeneration. CTP and the associated statistical tools we have developed thus provide a powerful framework for analysing complex cell behaviour in vivo from 4D datasets. SummaryAnalysis of cell shape and movement from 3D time-lapsed datasets is currently very challenging. We therefore designed Cell Tracking Profiler for analysing cell behaviour from complex datasets and demonstrate its effectiveness by analysing stem cell behaviour during muscle regeneration in zebrafish.

cell biology↗

bioRxiv2019

β-glucan dependent shuttling of conidia from neutrophils to macrophages occurs during fungal infection establishment

The initial host response to fungal pathogen invasion is critical to infection establishment and outcome. However, the diversity of leukocyte-pathogen interactions is only recently being appreciated. We describe a new form of interleukocyte conidial exchange called \"shuttling\". In Talaromyces marneffei and Aspergillus fumigatus zebrafish in vivo infections, live imaging demonstrated conidia initially phagocytosed by neutrophils were transferred to macrophages. Shuttling is unidirectional, not a chance event, involves alterations of phagocyte mobility, inter-cellular tethering, and phagosome transfer. Shuttling kinetics were fungal species-specific, implicating a fungal determinant. {beta}-glucan serves as a fungal-derived signal sufficient for shuttling. Murine phagocytes also shuttled in vitro. The impact of shuttling for microbiological outcomes of in vivo infections is difficult to specifically assess experimentally, but for these two pathogens, shuttling augments initial conidial redistribution away from fungicidal neutrophils into the favourable macrophage intracellular niche. Shuttling is a frequent host/pathogen interaction contributing to fungal infection establishment patterns.

cell biology↗

bioRxiv2018

Modelling the nuclear envelope of HeLa cells

This paper describes a framework for the automatic segmentation of the nuclear envelope of cancerous HeLa cells and the modelling of the volumetric shape against an ellipsoid. The framework is automatic and unsupervised and reported a Jaccard Similarity Index of 0.968 against a manual segmentation. The modelling of the surface provides a visual display of the variations, both smooth and rugged over the surface, and measurements can be extracted with the expectation that they can correlate with the biological characteristics of the cells.

cancer biology↗