CometAnalyser : A user-friendly, open-source deep-learning microscopy tool for quantitative comet assay analysis

Comet assay provides an easy solution to estimate DNA damage in single cells through microscopy assessment. It is widely used in the analysis of genotoxic damages induced by radiotherapy or chemotherapeutic agents. DNA damage is quantified at the single-cell level by computing the displacement between the genetic material within the nucleus, typically called ``comet head", and the genetic material in the surrounding part of the cell, considered as the ``comet tail". Today, the number of works based on Comet Assay analyses is really impressive. In this work, besides revising the solutions available to obtain reproducible and reliable quantitative data, we developed an easy-to-use tool named CometAnalyser. It is designed for the analysis of both fluorescent and silver-stained wide-field microscopy images and allows to automatically segment and classify the comets, besides extracting Tail Moment and several other intensity/morphological features for performing statistical analysis. CometAnalyser is an open-source deep-learning tool. It works with Windows, Macintosh, and UNIX-based systems. Source code, standalone versions, user manual, sample images, video tutorial and further documentation are freely available at: https://sourceforge.net/p/cometanalyser. (c) 2022 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY license (http://creativecommons. org/licenses/by/4.0/).Peer reviewe

Actin disassembly by cofilin, coronin, and Aip1 occurs in bursts and is inhibited by barbed-end cappers

Turnover of actin filaments in cells requires rapid actin disassembly in a cytoplasmic environment that thermodynamically favors assembly because of high concentrations of polymerizable monomers. We here image the disassembly of single actin filaments by cofilin, coronin, and actin-interacting protein 1, a purified protein system that reconstitutes rapid, monomer-insensitive disassembly (Brieher, W.M., H.Y. Kueh, B.A. Ballif, and T.J. Mitchison. 2006. J. Cell Biol. 175:315–324). In this three-component system, filaments disassemble in abrupt bursts that initiate preferentially, but not exclusively, from both filament ends. Bursting disassembly generates unstable reaction intermediates with lowered affinity for CapZ at barbed ends. CapZ and cytochalasin D (CytoD), a barbed-end capping drug, strongly inhibit bursting disassembly. CytoD also inhibits actin disassembly in mammalian cells, whereas latrunculin B, a monomer sequestering drug, does not. We propose that bursts of disassembly arise from cooperative separation of the two filament strands near an end. The differential effects of drugs in cells argue for physiological relevance of this new disassembly pathway and potentially explain discordant results previously found with these drugs

Development and standardization of a protocol for sperm cryopreservation of two important commercial oyster species

Dissertação de mestrado, Aquacultura e Pescas, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2015Aquaculture activities have a huge contribution for the world food production and their development is extremely necessary to answer to the lack of resources, especially to the demand for seafood. Bivalve production, especially Crassostrea angulata (Portuguese oyster) has been practiced from long ago, and although its production suffered several constraints, in recent years it has been increasing the interests in recovering production and in preserving nature populations. In this sense, new research needs to guarantee an efficient and economically viable production, contributing to a relatively new environmental concern: wild population restoration. Nowadays, pure wild populations of Crassostrea angulata are rare to find due to multiple factors that affected this oyster industry. Cryopreservation technology could promote alternative techniques to contribute for the resource management efficiency of the Portuguese oyster and associated economic activity. In this sense, standardization of procedures is important for Crassostrea genus. At the present there are no cryopreservation reports on Crassostrea angulata sperm, and therefore, one of the objectives of this work is to design a cryopreservation protocol for this species, testing the more adequate cryoprotectant solution, its ideal concentration, different freezing rates and types of containers. In parallel, this stablished protocol was applied in Crassostrea gigas and compared to other previously published for this species. Analysis of motility, viability, agglutination and fertilizations were used as guides for the establishment of the protocol in C. angulata. Moreover, ATP content, DNA fragmentation and lipid peroxidation were done in order to standardize the same protocol for both species. Movement analysis were assessed by CASA system, viability through common staining techniques and flow cytometer, agglutination was quantified according to the scale developed by Dong et al., (2007), ATP content determined by bioluminescence, Comet assay was performed to quantify the DNA fragmentation and lipid peroxidation determined spectrophotometrically by measuring the absorbance of the malondialdehyde (MDA). Significant differences were observed (p<0.05) for lipid peroxidation and fertilization trials whereas ATP content and fragmentation of DNA of the cryopreserved samples did not differ significantly from the control. In C. gigas, the same analysis were performed and did not reveal post-thaw quality differences in the samples cryopreserved with 10% DMSO. The established protocol revealed to be effective and with a low degree of cellular damage on C. angulata sperm and, at the same time, viable to apply in other species, such as Crassostrea gigas

Chemical genetic identification of CDKL5 substrates reveals its role in neuronal microtubule dynamics.

Loss-of-function mutations in CDKL5 kinase cause severe neurodevelopmental delay and early-onset seizures. Identification of CDKL5 substrates is key to understanding its function. Using chemical genetics, we found that CDKL5 phosphorylates three microtubule-associated proteins: MAP1S, EB2 and ARHGEF2, and determined the phosphorylation sites. Substrate phosphorylations are greatly reduced in CDKL5 knockout mice, verifying these as physiological substrates. In CDKL5 knockout mouse neurons, dendritic microtubules have longer EB3-labelled plus-end growth duration and these altered dynamics are rescued by reduction of MAP1S levels through shRNA expression, indicating that CDKL5 regulates microtubule dynamics via phosphorylation of MAP1S. We show that phosphorylation by CDKL5 is required for MAP1S dissociation from microtubules. Additionally, anterograde cargo trafficking is compromised in CDKL5 knockout mouse dendrites. Finally, EB2 phosphorylation is reduced in patient-derived human neurons. Our results reveal a novel activity-dependent molecular pathway in dendritic microtubule regulation and suggest a pathological mechanism which may contribute to CDKL5 deficiency disorder

Autophagy gene haploinsufficiency drives chromosome instability, increases migration, and promotes early ovarian tumors.

Autophagy, particularly with BECN1, has paradoxically been highlighted as tumor promoting in Ras-driven cancers, but potentially tumor suppressing in breast and ovarian cancers. However, studying the specific role of BECN1 at the genetic level is complicated due to its genomic proximity to BRCA1 on both human (chromosome 17) and murine (chromosome 11) genomes. In human breast and ovarian cancers, the monoallelic deletion of these genes is often co-occurring. To investigate the potential tumor suppressor roles of two of the most commonly deleted autophagy genes in ovarian cancer, BECN1 and MAP1LC3B were knocked-down in atypical (BECN1+/+ and MAP1LC3B+/+) ovarian cancer cells. Ultra-performance liquid chromatography mass-spectrometry metabolomics revealed reduced levels of acetyl-CoA which corresponded with elevated levels of glycerophospholipids and sphingolipids. Migration rates of ovarian cancer cells were increased upon autophagy gene knockdown. Genomic instability was increased, resulting in copy-number alteration patterns which mimicked high grade serous ovarian cancer. We further investigated the causal role of Becn1 haploinsufficiency for oncogenesis in a MISIIR SV40 large T antigen driven spontaneous ovarian cancer mouse model. Tumors were evident earlier among the Becn1+/- mice, and this correlated with an increase in copy-number alterations per chromosome in the Becn1+/- tumors. The results support monoallelic loss of BECN1 as permissive for tumor initiation and potentiating for genomic instability in ovarian cancer

SpheroScan: a user-friendly deep learning tool for spheroid image analysis.

BACKGROUND In recent years, 3-dimensional (3D) spheroid models have become increasingly popular in scientific research as they provide a more physiologically relevant microenvironment that mimics in vivo conditions. The use of 3D spheroid assays has proven to be advantageous as it offers a better understanding of the cellular behavior, drug efficacy, and toxicity as compared to traditional 2-dimensional cell culture methods. However, the use of 3D spheroid assays is impeded by the absence of automated and user-friendly tools for spheroid image analysis, which adversely affects the reproducibility and throughput of these assays. RESULTS To address these issues, we have developed a fully automated, web-based tool called SpheroScan, which uses the deep learning framework called Mask Regions with Convolutional Neural Networks (R-CNN) for image detection and segmentation. To develop a deep learning model that could be applied to spheroid images from a range of experimental conditions, we trained the model using spheroid images captured using IncuCyte Live-Cell Analysis System and a conventional microscope. Performance evaluation of the trained model using validation and test datasets shows promising results. CONCLUSION SpheroScan allows for easy analysis of large numbers of images and provides interactive visualization features for a more in-depth understanding of the data. Our tool represents a significant advancement in the analysis of spheroid images and will facilitate the widespread adoption of 3D spheroid models in scientific research. The source code and a detailed tutorial for SpheroScan are available at https://github.com/FunctionalUrology/SpheroScan

Information recovery from low coverage whole-genome bisulfite sequencing.

The cost of whole-genome bisulfite sequencing (WGBS) remains a bottleneck for many studies and it is therefore imperative to extract as much information as possible from a given dataset. This is particularly important because even at the recommend 30X coverage for reference methylomes, up to 50% of high-resolution features such as differentially methylated positions (DMPs) cannot be called with current methods as determined by saturation analysis. To address this limitation, we have developed a tool that dynamically segments WGBS methylomes into blocks of comethylation (COMETs) from which lost information can be recovered in the form of differentially methylated COMETs (DMCs). Using this tool, we demonstrate recovery of ∼30% of the lost DMP information content as DMCs even at very low (5X) coverage. This constitutes twice the amount that can be recovered using an existing method based on differentially methylated regions (DMRs). In addition, we explored the relationship between COMETs and haplotypes in lymphoblastoid cell lines of African and European origin. Using best fit analysis, we show COMETs to be correlated in a population-specific manner, suggesting that this type of dynamic segmentation may be useful for integrated (epi)genome-wide association studies in the future

깊은 신경망을 이용한 강인한 특징 학습

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2016. 8. 윤성로.최근 기계 학습의 발전으로 인공 지능은 우리에게 한 걸음 더 가까이 다가오게 되었다. 특히 자율 주행이나 게임 플레이 등 최신 인공 지능 프레임워크들에 있어서, 딥 러닝이 중요한 역할을 하고 있는 상황이다. 딥 러닝이란 multi-layered neural networks 과 관련된 기술들을 총칭하는 용어로서, 데이터의 양이 급속하게 증가하며, 사전 지식들이 축적되고, 효율적인 학습 알고리즘들이 개발되며, 고급 하드웨어들이 만들어짐에 따라 빠르게 변화하고 있다. 현재 딥 러닝은 대부분의 인식 문제에서 최첨단 기술로 활용되고 있다. 여러 레이어로 구성된 깊은 신경망은 많은 양의 파라미터를 학습하기 때문에, 방대한 파라미터 집합 속에서 좋은 해를 효율적으로 찾아내는 것이 중요하다. 본 논문에서는 깊은 신경망의 세 가지 이슈에 대해 접근하며, 그것들을 해결하기 위한 regularization 기법들을 제안한다. 첫째로, 신경망 구조는 adversarial perturbations 이라는 내재적인 blind spots 들에 많이 노출되어 있다. 이러한 adversarial perturbations 에 강인한 신경망을 만들기 위하여, 학습 샘플과 그것의 adversarial perturbations 와의 차이를 최소화하는 manifold loss term을 목적 함수에 추가하였다. 둘째로, restricted Boltzmann machines 의 학습에 있어서, 상대적으로 작은 크기를 가지는 클래스를 학습하는 데에 기존의 contrastive divergence 알고리즘은 한계점을 가지고 있었다. 본 논문에서는 작은 클래스에 더 높은 학습 가중치를 부여하는 boosting 개념과 categorical features를 가진 데이터에 적합한 새로운 regularization 기법을 조합하여 기존의 한계점에 접근하였다. 마지막으로, 신경망의 파라미터를 학습하기에 충분하지 않은 데이터가 주어진 경우, 더 정교한 data augmentation 기법을 다룬다. 샘플의 차원이 많을수록, 데이터 생성의 기저에 깔려있는 사전 지식을 활용하여 augmentation을 하는 것이 더욱 더 필요하다. 나아가, 본 논문은 junction splicing signals 학습을 위한 첫 번째 깊은 신경망 모델링 결과를 제시하고 있다. Junction prediction 문제는 positive 샘플 수가 매우 적어 패턴 모델링이 힘들며, 이는 생명정보학 분야에서 가장 중요한 문제 중 하나로서, 전체 gene expression process 를 이해하는 첫 걸음이라고 할 수 있다. 요약하면, 본 논문은 딥 러닝으로 이미지와 대용량 유전체 데이터를 위한 효과적인 표현법을 학습할 수 있는 regularization 기법들을 제안하였으며, 유명한 벤치마크 데이터와 biomedical imaging 데이터를 사용하여 그 실효성을 검증하였다.Recent advances in machine learning continue to bring us closer to artificial intelligence. In particular, deep learning plays a key role in cutting-edge frameworks such as autonomous driving and game playing. Deep learning refers to a class of multi-layered neural networks, which is rapidly evolving as the amount of data increases, prior knowledge builds up, efficient training schemes are being developed, and high-end hardwares are being build. Currently, deep learning is a state-of-the-art technique for most recognition tasks. As deep neural networks learn many parameters, there has been a variety of attempts to obtain reasonable solutions over a wide search space. In this dissertation, three issues in deep learning are discussed and approaches to solve them with regularization techniques are suggested. First, deep neural networks expose the problem of intrinsic blind spots called adversarial perturbations. Thus, we must construct neural networks that resist the directions of adversarial perturbations by introducing an explicit loss term to minimize the differences between the original and adversarial samples. Second, training restricted Boltzmann machines show limited performance when handling minority samples in class-imbalanced datasets. Our approach addresses this limitation and is combined with a new regularization concept for datasets that have categorical features. Lastly, insufficient data handling is required to be more sophisticated when deep networks learn numerous parameters. Given high-dimensional samples, we must augment datasets with adequate prior knowledge to estimate a high-dimensional distribution. Furthermore, this dissertation shows the first application of deep belief networks to identifying junction splicing signals. Junction prediction is one of the major problems in the field of bioinformatics, and is a starting point to understanding the entire gene expression process. In summary, this dissertation proposes a set of deep learning regularization schemes that can learn the meaningful representation underlying large-scale genomic datasets and image datasets. The effectiveness of these methods was confirmed with a number of experimental studies.Chapter 1 Introduction 1 1.1 Deep neural networks 1 1.2 Issue 1: adversarial examples handling 3 1.3 Issue 2: class-imbalance handling 5 1.4 Issue 3: insufficient data handling 5 1.5 Organization 6 Chapter 2 Background 10 2.1 Basic operations for deep networks 10 2.2 History of deep networks 12 2.3 Modern deep networks 14 2.3.1 Contrastive divergence 16 2.3.2 Deep manifold learning 18 Chapter 3 Adversarial examples handling 20 3.1 Introduction 20 3.2 Methods 21 3.2.1 Manifold regularized networks 21 3.2.2 Generation of adversarial examples 25 3.3 Results and discussion 26 3.3.1 Improved classification performance 28 3.3.2 Disentanglement and generalization 30 3.4 Summary 33 Chapter 4 Class-imbalance handling 35 4.1 Introduction 35 4.1.1 Numerical interpretation of DNA sequences 37 4.1.2 Review of junction prediction problem 41 4.2 Methods 44 4.2.1 Boosted contrastive divergence with categorical gradients 44 4.2.2 Stacking and fine-tuning 46 4.2.3 Initialization and parameter setting 47 4.3 Results and discussion 47 4.3.1 Experiment preparation 47 4.3.2 Improved prediction performance and runtime 49 4.3.3 More robust prediction by proposed approach 51 4.3.4 Effects of regularization on performance 53 4.3.5 Efficient RBM training by boosted CD 54 4.3.6 Identification of non-canonical splice sites 57 4.4 Summary 58 Chapter 5 Insufficient data handling 60 5.1 Introduction 60 5.2 Backgrounds 62 5.2.1 Understanding comets 62 5.2.2 Assessing DNA damage from tail shape 65 5.2.3 Related image processing techniques 66 5.3 Methods 68 5.3.1 Preprocessing 70 5.3.2 Binarization 70 5.3.3 Filtering and overlap correction 72 5.3.4 Characterization and classification 75 5.4 Results and discussion 76 5.4.1 Test data preparation 76 5.4.2 Binarization 77 5.4.3 Robust identification of comets 79 5.4.4 Classification 81 5.4.5 More accurate characterization by DeepComet 82 5.5 Summary 85 Chapter 6 Conclusion 87 6.1 Dissertation summary 87 6.2 Future work 89 Bibliography 91Docto