Predicting Proteome-Early Drug Induced Cardiac Toxicity Relationships (Pro-EDICToRs) with Node Overlapping Parameters (NOPs) of a new class of Blood Mass-Spectra graphs

The 11th International Electronic Conference on Synthetic Organic Chemistry session Computational ChemistryBlood Serum Proteome-Mass Spectra (SP-MS) may allow detecting Proteome-Early Drug Induced Cardiac Toxicity Relationships (called here Pro-EDICToRs). However, due to the thousands of proteins in the SP identifying general Pro-EDICToRs patterns instead of a single protein marker may represents a more realistic alternative. In this sense, first we introduced a novel Cartesian 2D spectrum graph for SP-MS. Next, we introduced the graph node-overlapping parameters (nopk) to numerically characterize SP-MS using them as inputs to seek a Quantitative Proteome-Toxicity Relationship (QPTR) classifier for Pro-EDICToRs with accuracy higher than 80%. Principal Component Analysis (PCA) on the nopk values present in the QPTR model explains with one factor (F1) the 82.7% of variance. Next, these nopk values were used to construct by the first time a Pro-EDICToRs Complex Network having nodes (samples) linked by edges (similarity between two samples). We compared the topology of two sub-networks (cardiac toxicity and control samples); finding extreme relative differences for the re-linking (P) and Zagreb (M2) indices (9.5 and 54.2 % respectively) out of 11 parameters. We also compared subnetworks with well known ideal random networks including Barabasi-Albert, Kleinberg Small World, Erdos-Renyi, and Epsstein Power Law models. Finally, we proposed Partial Order (PO) schemes of the 115 samples based on LDA-probabilities, F1-scores and/or network node degrees. PCA-CN and LDA-PCA based POs with Tanimoto’s coefficients equal or higher than 0.75 are promising for the study of Pro-EDICToRs. These results shows that simple QPTRs models based on MS graph numerical parameters are an interesting tool for proteome researchThe authors thank projects funded by the Xunta de Galicia (PXIB20304PR and BTF20302PR) and the Ministerio de Sanidad y Consumo (PI061457). González-Díaz H. acknowledges tenure track research position funded by the Program Isidro Parga Pondal, Xunta de Galici

Deep Learning in Single-Cell Analysis

Single-cell technologies are revolutionizing the entire field of biology. The large volumes of data generated by single-cell technologies are high-dimensional, sparse, heterogeneous, and have complicated dependency structures, making analyses using conventional machine learning approaches challenging and impractical. In tackling these challenges, deep learning often demonstrates superior performance compared to traditional machine learning methods. In this work, we give a comprehensive survey on deep learning in single-cell analysis. We first introduce background on single-cell technologies and their development, as well as fundamental concepts of deep learning including the most popular deep architectures. We present an overview of the single-cell analytic pipeline pursued in research applications while noting divergences due to data sources or specific applications. We then review seven popular tasks spanning through different stages of the single-cell analysis pipeline, including multimodal integration, imputation, clustering, spatial domain identification, cell-type deconvolution, cell segmentation, and cell-type annotation. Under each task, we describe the most recent developments in classical and deep learning methods and discuss their advantages and disadvantages. Deep learning tools and benchmark datasets are also summarized for each task. Finally, we discuss the future directions and the most recent challenges. This survey will serve as a reference for biologists and computer scientists, encouraging collaborations.Comment: 77 pages, 11 figures, 15 tables, deep learning, single-cell analysi

Leveraging single-cell genomics to uncover clinical and preclinical responses to cancer immunotherapy

Immune checkpoint inhibitors (ICIs) provide durable clinical responses in about 20% of cancer patients, but have been largely ineffective for non-immunogenic cancers that lack intratumoral T cells. Most tumors have somatic mutations that encode for mutant proteins that are tumor-specific and not expressed on normal cells (termed neoantigens). Cancers, such as melanoma, with the highest mutational burdens are more likely to respond to single agent ICIs. However, most cancers, including pancreatic ductal adenocarcinoma (PDAC), have lower mutational loads, resulting in fewer T cells infiltrating the tumor. Studies have previously demonstrated that an allogeneic GM-CSF-based vaccine enhances T cell infiltration into human pancreatic cancer. Recent work with Panc02 cells, which express around 60 neoantigens similar to human PDAC, showed that PancVAX, a neoantigen-targeted vaccine, when paired with immune modulators cleared tumors in Panc02-bearing mice. This data suggests that cancer vaccines targeting tumor neoantigens induce neoepitope-specific T cells, which can be further activated by ICIs, leading to tumor rejection. Currently, the impact of ICIs and neoantigen-targeted vaccines on immune cell expression states and the underlying mechanism of therapeutic response remains poorly defined. Comprehensive characterization of responding immune cells, particularly T cells, will be critical in understanding mechanisms of response and providing a rationale for combinatorial therapies. In this work, we develop innovative computational methods and analysis pipelines to analyze the tumor-immune microenvironment at single-cell resolution. We establish an algorithm to quantify differential heterogeneity in single-cell RNA-seq data, demonstrate the use of non-negative matrix factorization and transfer learning algorithms to identify previously unknown and conserved ICI responses between species, and develop a novel algorithm to physicochemically compare single-cell T cell receptor sequences. We leverage these methods in various contexts to yield new insight into the biological mechanisms underlying positive immunotherapeutic responses in diverse tumor types, including PDAC

Consequences of local and global chromatin mechanics to adaption and genome stability in the budding yeast Saccharomyces cerevisiae

Le génome de la levure de boulanger Saccharomyces cerevisiae a évolué à partir d'un ancêtre chez lequel une profonde décompaction du génome s'est produite à la suite de la perte de la méthylation de la lysine 9 de l'histone H3, il y a environ 300 millions d'années. Il a été proposé que cette décompaction du génome a entraîné une capacité accrue des levures à évoluer par des mécanismes impliquant des taux de recombinaison méiotique et de mutation exceptionnellement élevés. La capacité à évoluer accrue qui en résulte pourrait avoir permis des adaptations uniques, qui en ont fait un eucaryote modèle idéal et un outil biotechnologique. Dans cette thèse, je présenterai deux exemples de la façon dont les adaptations locales et globales du génome se reflètent dans les changements des propriétés mécaniques de la chromatine qui, à leur tour, indiquent un phénomène de séparation de phase causée par les modifications post-traductionnelles des histones et des changements dans les taux d'échange des histones. Dans un premier manuscrit, je présente des preuves d'un mécanisme par lequel la relocalisation du locus INO1, gène actif répondant à la déplétion en inositol, du nucléoplasme vers l'enveloppe nucléaire, augmente la vitesse d'adaptation et la robustesse métabolique aux ressources fluctuantes, en augmentant le transport des ARNm vers le cytosol et leur traduction. La répartition d'INO1 vers l'enveloppe nucléaire est déterminée par une augmentation locale des taux d'échange d'histones, ce qui entraîne sa séparation de phase du nucléoplasme en une phase de faible densité plus proche de la périphérie nucléaire. J'ai quantifié les propriétés mécaniques de la chromatine du locus du gène dans les états réprimé et actif en analysant le déplacement de 128 sites LacO fusionnés au gène liant LacI-GFP en calculant diffèrent paramètres tel que la constante de ressort effective et le rayons de confinement du locus. De plus, j'ai mesuré l'amplitude et le taux d'expansion en fonction du temps du réseau LacO et j'ai observé une diminution significative du locus à l'état actif, ce qui est cohérent avec le comportement de ressort entropique de la chromatine décompactée. J'ai montré que les séquences d'éléments en cis dans le promoteur du locus, essentielles à la séparation de phase, sont des sites de liaison pour les complexes de remodelage de la chromatine effectuant l'acétylation des histones. Ces modifications de la chromatine entraînent une augmentation des taux d'échanges des sous-unités des complexes d'histones, et une séparation de phase locale de la chromatine. Enfin, je présente l’analyse de simulations in silico qui montrent que la séparation de phase locale de la chromatine peut être prédite à partir d'un modèle de formation/disruption des interactions multivalentes protéine-protéine et protéine-ADN qui entraîne une diminution de la dynamique de l'ADN. Ces résultats suggèrent un mécanisme général permettant de contrôler la formation rapide des domaines de la chromatine, bien que les processus spécifiques contribuant à la diminution de la dynamique de l'ADN restent à étudier. Dans un second manuscrit, je décris comment nous avons induit la « retro-évolution » de la levure en réintroduisant la méthylation de la lysine 9 de l'histone H3 par l'expression de deux gènes de la levure Schizosaccaromyces pombe Spswi6 et Spclr4. Le mutant résultant présente une augmentation de la compaction de la chromatine, ce qui entraîne une réduction remarquable des taux de mutation et de recombinaison. Ces résultats suggèrent que la perte de la méthylation de la lysine 9 de l'histone H3 pourrait avoir augmenté la capacité à l'évoluer. La stabilité inhabituelle du génome conférée par ces mutations pourrait être utile pour l'ingénierie métabolique de S. cerevisiae, dans laquelle il est difficile de maintenir des gènes exogènes intégrés pour les applications de nombreux processus biotechnologiques courants tels que la production de vin, de bière, de pain et de biocarburants. Ces résultats soulignent l'influence des propriétés physiques d'un génome sur son architecture et sa fonction globales.The genome of the budding yeast Saccharomyces cerevisiae evolved from an ancestor in which a profound genome decompaction occurred as the result of the loss of histone H3 lysine 9 methylation, approximately 300 million years ago. This decompaction may have resulted in an increased capacity of yeasts to evolve by mechanisms that include unusually high meiotic recombination and mutation rates. Resultant increased evolvability may have enabled unique adaptations, which have made it an ideal model eukaryote and biotechnological tool. In this thesis I will present two examples of how local and global genome adaptations are reflected in changes in the mechanical properties of chromatin. In a first manuscript, I present evidence for a mechanism by which partitioning of the active inositol depletion-responsive gene locus INO1 from nucleoplasm to the nuclear envelope increases the speed of adaptation and metabolic robustness to fluctuating resources, by increasing mRNA transport to the cytosol and their translation. Partitioning of INO1 to the nuclear envelope is driven by a local increase in histone exchange rates, resulting in its phase separation from the nucleoplasm into a low-density phase closer to the nuclear periphery. I quantified the mechanical properties of the gene locus chromatin in repressed and active states by monitoring mean-squared displacement of an array of 128 LacO sites fused to the gene binding LacI-GFP and calculating effective spring constants and radii of confinement of the array. Furthermore, I measured amplitude and rate of time-dependent expansion of the LacO array, and observed a significant decrease for the active-state locus which is consistent with entropic spring behavior of decompacted chromatin. I showed that cis element sequences in the promoter and upstream of the locus that are essential to phase separation are binding sites for chromatin remodeling complexes that perform histone acetylation among other modifications that result in increased histone complex exchange rates, and consequent local chromatin phase separation. Finally, I present analytical simulations that show that local phase separation of chromatin can be predicted from a model of formation/disruption of multivalent protein-protein and protein-DNA interactions that results in decreased DNA dynamics. These results suggest a general mechanism to control rapid formation of chromatin domains, although the specific processes contributing to the decreased DNA dynamics remain to be investigated. In a second manuscript, I describe how we retro-evolutionarily engineered yeast by reintroducing histone H3 lysine 9 methylation through the expression of two genes from the yeast Schizosaccaromyces pombe Spswi6 and Spclr4. This mutant shows an increase in compaction, resulting in remarkable reduced mutation and recombination rates. These results suggest that loss of histone H3 lysine 9 methylation may have increased evolvability. The unusual genome stability imparted by these mutations could be of value to metabolically engineering S. cerevisiae, in which it is difficult to maintain integrated exogenous genes for applications for many common biotechnological processes such as wine, beer, bread, and biofuels production. These results highlight the influence of the physical properties of a genome on its overall architecture and function

Perturbing and imaging nuclear compartments to reveal mechanisms of transcription regulation and telomere maintenance

The cell nucleus is organized into functional domains that form around chromatin, which serves as a scaffold composed of DNA, proteins, and associated RNAs. On the 0.1-1 µm mesoscale these domains can form spatially defined compartments with distinct composition and properties that enrich specific genomic activities like transcription, chromatin modification or DNA repair. In addition, extrachromosomal DNA elements and RNAs can separate from the chromatin template and assemble with proteins into nuclear bodies. The resulting accumulations of proteins and nucleic acids in the nucleus modulate chromatin-templated processes and their organization. The assembly of these compartments occurs in a self-organizing manner via direct and indirect binding of proteins to DNA and/or RNA. Recently, it has been proposed that multivalent interactions drive compartmentalization by inducing phase separation with a non-stoichiometric accumulation of factors into biomolecular condensates. Despite the importance of compartments for genome regulation, insights into their structure and material properties and how these affect their function is limited. To address this issue, it is important to devise approaches that can perturb nuclear compartments in a targeted manner, while also measuring changes in genome activities within the same cell. In this thesis, the methodology to reveal the underlying structure-function relationships of nuclear compartments has been advanced and applied to compartments involved in activation and silencing of chromatin, and telomere maintenance in cancer cells. I first established a toolbox of chromatin effector constructs to probe and perturb properties of nuclear compartments in living cells that comprised different combinations of DNA binding, transcription activation and light-dependent interaction domains. In addition, I developed workflows to quantitatively assess relevant compartment features by fluorescence microscopy. These methods were employed to study the compaction mechanism of mouse pericentric heterochromatin (PCH) foci and to investigate the interplay between transcriptional co-activators, phase separation and transcription at an inducible reporter gene cluster. It revealed determinants of PCH compaction and identified differential co-activator usage and multivalent interactions as contributors to transcription factor (TF) strength. The results furthermore challenged the model of TF phase separation as a general positive driver of gene transcription. In the second part, I focused on exploiting the detection of compartments for measuring activity of the alternative lengthening of telomeres (ALT) pathway used by cancer cells to extend their telomeres in absence of telomerase. I developed ALT-FISH, a scalable and quantitative imaging assay that detects ALT pathway-specific compartments containing large amounts of single-stranded telomeric nucleic acids. I applied the method to cell line models from different cancer entities and to tumor tissue from leiomyosarcoma and neuroblastoma patients. By devising automated ALT-FISH data acquisition and analysis IV workflows, I implemented an approach, which enabled ALT activity measurements in hundreds of thousands of single cells. These technological advancements provided a quantitative description of ALT activity at single cell resolution and were used to characterize the spatial distribution of ALT activity in relation to other biological features and in response to perturbations. Finally, a novel approach for studying the regulation of ALT in tumors could be established by integrating the method with the spatially resolved detection of single cell transcriptomes. In summary, this thesis introduced and utilized several methods to establish connections between nuclear compartment organization, chromatin features, transcription regulation, and telomere maintenance. These perturbation and imaging techniques are versatile and may be applied to dissect nuclear activities related to other compartments and biological model systems. Furthermore, the detection of ALT activity has demonstrated that compartments can offer valuable biological insights into how phenotypic cellular heterogeneity is encoded and linked to diseases such as cancer

The Affine Uncertainty Principle, Associated Frames and Applications in Signal Processing

Uncertainty relations play a prominent role in signal processing, stating that a signal can not be simultaneously concentrated in the two related domains of the corresponding phase space. In particular, a new uncertainty principle for the affine group, which is directly related to the wavelet transform has lead to a new minimizing waveform. In this thesis, a frame construction is proposed which leads to approximately tight frames based on this minimizing waveform. Frame properties such as the diagonality of the frame operator as well as lower and upper frame bounds are analyzed. Additionally, three applications of such frame constructions are introduced: inpainting of missing audio data, detection of neuronal spikes in extracellular recorded data and peak detection in MALDI imaging data

Spatial statistics from hyperplexed immunofluorescence images: to elucidate tumor microenvironment, to characterize intratumor heterogeneity, and to predict metastatic potential

The composition of the tumor microenvironment (TME)–the malignant, immune, and stromal cells implicated in tumor biology as well as the extracellular matrix and noncellular elements–and the spatial relationships between its constituents are important diagnostic biomarkers for cancer progression, proliferation, and therapeutic response. In this thesis, we develop methods to quantify spatial intratumor heterogeneity (ITH). We apply a novel pattern recognition framework to phenotype cells, encode spatial information, and calculate pairwise association statistics between cell phenotypes in the tumor using pointwise mutual information. These association statistics are summarized in a heterogeneity map, used to compare and contrast cancer subtypes and identify interaction motifs that may underlie signaling pathways and functional heterogeneity. Additionally, we test the prognostic power of spatial protein expression and association profiles for predicting clinical cancer staging and recurrence, using multivariate modeling techniques. By demonstrating the relationship between spatial ITH and outcome, we advocate this method as a novel source of information for cancer diagnostics. To this end, we have released an open-source analysis and visualization platform, THRIVE (Tumor Heterogeneity Research Image Visualization Environment), to segment and quantify multiplexed imaging samples, and assess underlying heterogeneity of those samples. The quantification of spatial ITH will uncover key spatial interactions, which contribute to disease proliferation and progression, and may confer metastatic potential in the primary neoplasm

Registration and 3D reconstruction of histological sections: application to mammary gland development

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, mayo de 200

AtomLbs: An Atom Based Convolutional Neural Network for Druggable Ligand Binding Site Prediction

Despite advances in drug research and development, there are few and ineffective treatments for a variety of diseases. Virtual screening can drastically reduce costs and accelerate the drug discovery process. Binding site identification is one of the initial and most important steps in structure-based virtual screening. Identifying and defining protein cavities that are likely to bind to a small compound is the objective of this task. In this research, we propose four different convolutional neural networks for predicting ligand-binding sites in proteins. A parallel optimized data pipeline is created to enable faster training of these neural network models on minimal hardware. The effectiveness of each method is assessed on well-established ligand binding site datasets. It is then compared with the state-of-the-art and widely used methods for ligand binding site identification. The result shows that our methods outperform most of the other methods and are comparable to the state-of-the-art methods