Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
Browse
Search Results
- Identifying a subsequent bleaching response in the Acropora genus through comparative transcriptomics(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-12-01) Jalife Gómez, Ariadna; Rangel Escareño, Claudia; mtyahinojosa, emipsanchez; Alvarado Cerón, Viridiana; Quintus Scheckthuber, Christian; Escuela de Ingeniería y Ciencias; Campus MonterreyRising sea temperatures trigger coral bleaching, a process where corals expel vital symbiotic algae from their tissues, leading to reef degradation worldwide. Coral reefs, not only as a source of cultural, economic, and societal benefits, but also as a crucial ecosystem to fight climate change (due to a larger oxygen production in a fraction of space that a forest requires) constantly experience coral bleaching events worldwide with less time between such events, and more intensity in the response severity, as four mass bleaching events have occurred thus far, threatening to conduct many relevant coral species to mass extinction events, a particularly impactful truth for acroporid corals. This thesis addresses the critical need to classify and characterize coral bleaching process by depicting molecular signatures of this phenomenon in Acropora corals, a genus known as reef-builder that is crucial for healthy reef ecosystems due to structure formation and shelter provision in marine ecosystems. Although extensive research has explored the molecular mechanisms of coral bleaching through transcriptomic studies, characterization of the bleaching response remains a challenge in relation with variation in identified mechanisms and lack of integrative efforts between different studies, hindering the development of preventive solutions rather than curative ones. Firstly, we created a dataset with previously processed transcriptomic data from geographically diverse Acropora species using 40 samples from simulated heat stress experiments that corresponded to transcriptomic studies from adult coral colonies with reports of sampling time. Secondly, we analyzed chronological activation of transcriptome patterns through time series for control samples, thermal stress and bleaching in Acropora, aiming to identify conserved molecular signatures regardless of geographical variation. Results revealed that despite heterogeneity present in the dataset, subsequent gene expression responses were identified through functional analysis for both control and heatstressed scenarios with additional validation of time dependence through comparison with a bleaching group, nonetheless, species-specific expression was also identified with a relevant impact of the bacterial component of the coral holobiont. By classifying bleaching responses, we can pave the way for a more targeted intervention strategy to inhibit coral bleaching at a critical juncture defined by gene expression patterns, regardless of environmental variability. The present work could contribute to further management strategies for coral reefs in response to climate change with an informed perspective in molecular terms.
- Enhancing single-cell and spatial transcriptomics analysis: the role of imputation and feature selection(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-06) Chacón Ramírez Denisse; Rangel Escareño, Claudia; emipsanchez; Gómez Romero, Laura Lucila; Hernández Lemus, Enrique; Reséndis Antonio, Osbaldo; School of Engineering and Sciences; Campus MonterreySingle-cell RNA sequencing (scRNA-seq) and spatial transcriptomics have revolutionized our understanding of cellular heterogeneity and tissue organization. However, extracting biological insights from these technologies remains challenging due to high-dimensional, sparse, and noisy data. Two critical but understudied problems hinder robust analysis: (1) the impact of feature selection strategies on cell-type identification, and (2) the role of data imputation in integrating scRNA-seq with spatial transcriptomics. While clustering and integration methods are widely benchmarked, the influence of pre-processing decision, such as using biologically informed marker genes or imputing missing values, remains poorly understood. This thesis addresses these gaps through systematic evaluations. This thesis addresses these knowledge gaps through systematic evaluations across diverse datasets and algorithms. First, we assess how different imputation algorithms (MAGIC, DCA, scPHENIX) affect the integration of scRNA-seq with spatial transcriptomics in both ways, cell-type deconvolution and spatial transcript prediction. Using 13 paired datasets and 10 integration tools, we found that imputation’s benefits depend on the task and algorithm. The results reveal that imputation benefits are highly context-dependent rather than universally beneficial. SpaGE consistently outperformed other methods for transcript prediction regardless of imputation status, while RCTD demonstrated superior performance for cell deconvolution tasks. Notably, we observed that imputation primarily enhances magnitude estimation rather than improving spatial pattern preservation. Second, we evaluate whether marker gene-based feature selection improves scRNA-seq clustering accuracy compared to standard approaches. By benchmarking seven algorithms(Seurat, SC3, CIDR, etc.) across five pancreatic datasets, we demonstrate that performance gains are algorithm, and dataset-dependent. SC3 and TSCAN benefited from marker gene selection across multiple datasets, while SIMLR showed dramatic dataset-dependent responses,yielding superior ARI scores (greater than 0.7) in some contexts but diminished performance in others. The Segerstolpe dataset showed consistent improvements across most algorithms when using marker genes, suggesting dataset-specific characteristics strongly influence optimal feature selection strategies. Our analysis further revealed that algorithms often identify fewer clusters than reference annotations, indicating challenges in resolving fine-grained pancreatic cell type heterogeneity. The results of this thesis emphasize that pre-processing choices must align with both analytical goals and dataset characteristics to unlock the full potential of single-cell technologies. This work provides an evidence-based framework for optimizing spatial transcriptomics and scRNA-seq analysis workflows, with implications for understanding tissue architecture and cellular dynamics across diverse biological systems.
- Modeling the relationship between the gut microbiome and progressive neurodegenerative diseases: case study Alzheimer’s disease(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-05-26) Trejo Castro, Alejandro Ismael; RANGEL ESCAREÑO, CLAUDIA; 200229; Rangel Escareño, Claudia; tolmquevedo; Alanis Funes, Gerardo Javier; Chávez Santoscoy, Rocío Alejandra; Fernández Figueroa, Edith Araceli; School of Engineering and Sciences; Campus MonterreyAlzheimer’s Disease (AD) has been known since 1906 and many of the symptoms and signs from the first case continue in the conceptualization of AD, such as memory loss, visuospatial disorders, impaired verbal communication, delirium, impotence and personality changes, such as depression and irritability, is the most common cause of dementia and neurodegenerative disease. It is expected to see an increment of up to 225% in the number of patients during a 40-year time frame (2010 - 2050). Clinically, the hallmark pathology of AD is the accumulation of amyloid-β (Aβ) protein fragments outside the neurons and accumulation of abnormal tau tangles within neurons. However, the microbiome composition is unique to a patient and, current studies have also proven the existence of a correlation with the microbiota that results in inflammation patterns and the accumulation of proteins related to AD. Nevertheless, no study so far has presented a model representing the interaction between the microbiota and the current tests to diagnose AD. In this study for the master’s program in Computer Science, we will approach a novel characterization of AD integrating clinical data, gut microbial metabolites and serum lipids metabolites. From a systems biology perspective, we intend to explain these covariates through machine learning and feature selection algorithms that would serve to find biomarkers between those who advance to the disease and those who does not. Data has been collected from various sources, the Alzheimer’s Disease Neuroimaging Initiative (ADNI), and the Alzheimer’s Disease Metabolomics Consortium (ADMC). Our findings suggest that the combination of gut microbial metabolites with the well-known neuropsychological tests could enhance the diagnosis and prediction of AD. This research project invite the researcher to carry out more experiments about the microbiome since we realized is becoming the key to better comprehend AD and probably other neurodegenerative diseases.