Tiago Esteves

Cyanobacteria use micro-optics to sense light direction

Bacterial phototaxis was first recognized over a century ago, but the method by which such small cells can sense the direction of illumination has remained puzzling. The unicellular cyanobacterium Synechocystis sp. PCC 6803 moves with Type IV pili and measures light intensity and color with a range of photoreceptors. Here, we show that individual Synechocystis cells do not respond to a spatiotemporal gradient in light intensity, but rather they directly and accurately sense the position of a light source. We show that directional light sensing is possible because Synechocystis cells act as spherical microlenses, allowing the cell to see a light source and move towards it. A high-resolution image of the light source is focused on the edge of the cell opposite to the source, triggering movement away from the focused spot. Spherical cyanobacteria are probably the world’s smallest and oldest example of a camera eye. DOI: http://dx.doi.org/10.7554/eLife.12620.001

An objective comparison of cell-tracking algorithms

We present a combined report on the results of three editions of the Cell Tracking Challenge, an ongoing initiative aimed at promoting the development and objective evaluation of cell segmentation and tracking algorithms. With 21 participating algorithms and a data repository consisting of 13 data sets from various microscopy modalities, the challenge displays todays state-of-the-art methodology in the field. We analyzed the challenge results using performance measures for segmentation and tracking that rank all participating methods. We also analyzed the performance of all of the algorithms in terms of biological measures and practical usability. Although some methods scored high in all technical aspects, none obtained fully correct solutions. We found that methods that either take prior information into account using learning strategies or analyze cells in a global spatiotemporal video context performed better than other methods under the segmentation and tracking scenarios included in the challenge.

MIQuant – Semi-Automation of Infarct Size Assessment in Models of Cardiac Ischemic Injury

Background The cardiac regenerative potential of newly developed therapies is traditionally evaluated in rodent models of surgically induced myocardial ischemia. A generally accepted key parameter for determining the success of the applied therapy is the infarct size. Although regarded as a gold standard method for infarct size estimation in heart ischemia, histological planimetry is time-consuming and highly variable amongst studies. The purpose of this work is to contribute towards the standardization and simplification of infarct size assessment by providing free access to a novel semi-automated software tool. The acronym MIQuant was attributed to this application. Methodology/Principal Findings Mice were subject to permanent coronary artery ligation and the size of chronic infarcts was estimated by area and midline-length methods using manual planimetry and with MIQuant. Repeatability and reproducibility of MIQuant scores were verified. The validation showed high correlation (r midline length = 0.981; r area = 0.970 ) and agreement (Bland-Altman analysis), free from bias for midline length and negligible bias of 1.21% to 3.72% for area quantification. Further analysis demonstrated that MIQuant reduced by 4.5-fold the time spent on the analysis and, importantly, MIQuant effectiveness is independent of user proficiency. The results indicate that MIQuant can be regarded as a better alternative to manual measurement. Conclusions We conclude that MIQuant is a reliable and an easy-to-use software for infarct size quantification. The widespread use of MIQuant will contribute towards the standardization of infarct size assessment across studies and, therefore, to the systematization of the evaluation of cardiac regenerative potential of emerging therapies.

Gradient convergence filters and a phase congruency approach for in vivo cell nuclei detection

Computational methods used in microscopy cell image analysis have largely augmented the impact of imaging techniques, becoming fundamental for biological research. The understanding of cell regulation processes is very important in biology, and in particular confocal fluorescence imaging plays a relevant role for the in vivo observation of cells. However, most biology researchers still analyze cells by visual inspection alone, which is time consuming and prone to induce subjective bias. This makes automatic cell image analysis essential for large scale, objective studies of cells. While the classic approach for automatic cell analysis is to use image segmentation, for in vivo confocal fluorescence microscopy images of plants, such approach is neither trivial nor is it robust to image quality variations. To analyze plant cells in in vivo confocal fluorescence microscopy images with robustness and increased performance, we propose the use of local convergence filters (LCF). These filters are based in gradient convergence and as such can handle illumination variations, noise and low contrast. We apply a range of existing convergence filters for cell nuclei analysis of the Arabidopsis thaliana plant root tip. To further increase contrast invariance, we present an augmentation to local convergence approaches based on image phase information. Through the use of convergence index filters we improved the results for cell nuclei detection and shape estimation when compared with baseline approaches. Using phase congruency information we were able to further increase performance by 11% for nuclei detection accuracy and 4% for shape adaptation. Shape regularization was also applied, but with no significant gain, which indicates shape estimation was good for the applied filters.

Macrophage interactions with polylactic acid and chitosan scaffolds lead to improved recruitment of human mesenchymal stem/stromal cells: a comprehensive study with different immune cells.

Despite the importance of immune cell–biomaterial interactions for the regenerative outcome, few studies have investigated how distinct three-dimensional biomaterials modulate the immune cell-mediated mesenchymal stem/stromal cells (MSC) recruitment and function. Thus, this work compares the response of varied primary human immune cell populations triggered by different model scaffolds and describes its functional consequence on recruitment and motility of bone marrow MSC. It was found that polylactic acid (PLA) and chitosan scaffolds lead to an increase in the metabolic activity of macrophages but not of peripheral blood mononuclear cells (PBMC), natural killer (NK) cells or monocytes. PBMC and NK cells increase their cell number in PLA scaffolds and express a secretion profile that does not promote MSC recruitment. Importantly, chitosan increases IL-8, MIP-1, MCP-1 and RANTES secretion by macrophages while PLA stimulates IL-6, IL-8 and MCP-1 production, all chemokines that can lead to MSC recruitment. This secretion profile of macrophages in contact with biomaterials correlates with the highest MSC invasion. Furthermore, macrophages enhance stem cell motility within chitosan scaffolds by 44% but not in PLA scaffolds. Thus, macrophages are the cells that in contact with engineered biomaterials become activated to secrete bioactive molecules that stimulate MSC recruitment.

Cancer Cell Detection and Morphology Analysis Based on Local Interest Point Detectors

The automatic analysis of cancer cells has gained increasing relevance given the amount of data that biology researchers have to analyze. However, most biology researchers still analyze cells by visual inspection alone, which is time consuming and prone to induce subjective bias. This makes automatic cell image analysis essential for large scale, objective studies of cells.

Cancer Cell Detection and Tracking Based on Local Interest Point Detectors

The automatic analysis of cell mobility has gained increasing relevance given the enormous amount of data that biology researchers have currently to analyze. However, most biology researchers still analyze cells by visual inspection alone, which is time consuming and prone to induce subjective bias. This makes automatic cell’s mobility analysis essential for large scale, objective studies of cells. To evaluate cancer cell’s mobility, biologists establish in vitro assays with cancer cells seeded on native surfaces or on surfaces coated with extracellular matrix components, recording time-lapse brightfield microscopy images. In such analysis only through the use of quantitative automatic analysis tools is it possible to gather evidence to firmly support biological findings.

Automatic and semi-automatic analysis of the extension of myocardial infarction in an experimental Murine model

Rodent models of myocardial infarction (MI) have been extensively used in biomedical research towards the implementation of novel regenerative therapies. Permanent ligation of the left anterior descending (LAD) coronary artery is a commonly used method for inducing MI both in rat and mouse. Post-mortem evaluation of the heart, particularly the MI extension assessment performed on histological sections, is a critical parameter for this experimental setting. MI extension, which is defined as the percentage of the left ventricle affected by the coronary occlusion, has to be estimated by identifying the infarcted- and the normal-tissue in each section. However, because it is a manual procedure it is time-consuming, arduous and prone to bias. Herein, we introduce semi-automatic and automatic approaches to perform segmentation which is then used to obtain the infarct extension measurement. Experimental validation is performed comparing the proposed approaches with manual annotation and a total error not exceeding 8% is reported in all cases.

Transfer Learning for the Recognition of Immunogold Particles in TEM Imaging

We present a (TL) framework based on (SDA) for the recognition of immunogold particles. These particles are part of a high-resolution method for the selective localization of biological molecules at the subcellular level only visible through (TEM). Four new datasets were acquired encompassing several thousands of immunogold particles. Due to the particles size (for a particular dataset a particle has a radius of 4 pixels in an image of size 4008\(\times \)2670) the annotation of these datasets is extremely time taking. Thereby, we apply a (TL) approach by reusing the learning model that can be used on other datasets containing particles of different (or similar) sizes. In our experimental study we verified that our (TL) framework outperformed the baseline (not involving TL) approach by more than 20% of accuracy on the recognition of immunogold particles.

Automatic myocardial infarction size extraction in an experimental murine model using an anatomical model

Experimental rodent models of induced ischemic injury have been extensively used in biomedical research to study molecular, cellular and histological alterations following myocar-dial infarction. These models are increasingly employed to assess the potential of newly developed therapies for functional restoration of the damaged heart. Such studies are based on myocardial infarction induction followed by different therapeutic interventions and subsequent analysis of the infarct size. This analysis is used to evaluate the extent to which such interventions meet recovery of the lost myocardial tissue. Infarct size is defined as the percentage of the left ventricle affected by coronary artery occlusion. The infarct size is traditionally estimated manually delineating the infarcted and normal tissue areas in the left ventricle of the excised heart. However, this is a time-consuming, arduous and prone to bias process. Herein, we developed an anatomic model, adapted through expectation maximization, which allows for fully automatic analysis of the data. Experimental validation is performed comparing the proposed approach with manual annotation. The results obtained through anatomical model adaptation were coherent with those manually obtained and the differences where never higher than 10%.