Quality by design-centered platform for the manufacturing of CAR-engineered NK cells and extracellular vesicles for immunotherapy

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Label-free discrimination of T and B lymphocyte activation based on vibrational spectroscopy: a machine learning approach

Publication . Ramalhete, Luís; Araújo, Rúben; Ferreira, Aníbal; Calado, Cecília

B and T-lymphocytes are major players of the specific immune system, responsible by an efficient response to target antigens. Despite the high relevance of these cells’ activation in diverse human pathophysiological processes, its analysis in clinical context presents diverse constraints. In the present work, MIR spectroscopy was used to acquire the cells molecular profile in a label-free, simple, rapid, economic, and high-throughput mode. Recurring to machine learning algorithms MIR data was subsequently evaluated. Models were developed based on specific spectral bands as selected by Gini index and the Fast Correlation Based Filter. To determine if it was, possible to predict from the spectra, if B and T lymphocyte were activated, and what was the molecular fingerprint of T- or B- lymphocyte activation. The molecular composition of activated lymphocytes was so different from naïve cells, that very good prediction models were developed with whole spectra (with AUC=0.98). Activated B lymphocytes also present a very distinct molecular profile in relation to activated T lymphocytes, leading to excellent prediction models, especially if based on target bands (AUC=0.99). The identification of critical target bands, according to the metabolic differences between B and T lymphocytes and in association with the molecular mechanism of the activation process highlighted bands associated to lipids and glycogen levels. The method developed presents therefore, appealing characteristics to promote a new diagnostic tool to analyze and discriminate B from T-lymphocytes.

2023-05Journal article

Restricted access

Discovery of infection biomarkers based on metabolomics

Publication . Alexandre, Tiago Francisco Rosa Domingues; Calado, Cecília Ribeiro da Cruz; Silvestre, Joana; Ricardo, António

Enquadramento e objetivos: Pacientes críticos de COVID-19 são regularmente admitidos nos cuidados intensivos com diversas complicações, necessitando de tratamentos mais invasivos. Para além disso, os pacientes estão expostos a uma ameaça iminente de infeções durante a sua estadia hospitalar. Estas infeções podem levar ao agravamento do estado de saúde do paciente, e tendo em conta o estado atual do paciente COVID-19 critico, pode ser fatal caso não seja devidamente identificada a presença de infeção e iniciado o tratamento mais adequado. Nesta tese, o foco foi dividido em dois objetivos: determinar uma metodologia capaz de identificar mais rapidamente um estado ativo de bacteremia no paciente COVID-19 critico; e identificar a tipologia de Gram da bactéria que originou a bacteremia. Métodos: Recorrendo-se ao método do espectrometria de FTIR, e testes Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA) e Linear Discriminant analysis (PCA-LDA), para realizar analise discriminante de uma amostra com objetivo de testar o método mais eficaz na discriminação entre pacientes com bacteremia (n=48) e pacientes sem bacteremia (n=54), e entre amostras com bactéria Gram-positiva (n=28) e bactéria Gram-negativa (n=20). Resultados: Através dos testes PCA e HCA não foi possível obter uma discriminação fidedigna nem entre amostras com e sem bacteremia, nem entre bactérias Gram-positivas e Gram-Negativas. A vasta variabilidade associada a amostras biológicas pode justificar este resultado. PCA-LDA, possibilitou resultados de 75% de eficácia na discriminação entre amostras de bacteremia e sem bacteremia, e uma eficácia de 85% na discriminação entre amostras com bactérias Gram-positivas e bactérias Gram-negativas. Conclusão: Os resultados apontam para a possibilidade da utilização da análise de espetro de espetrometria FTIR como um método apelador para o diagnóstico de bacteremia e classificação do tipo de bactéria, de uma forma simples e rápida, permitindo uma gestão mais eficiente deste tipo de pacientes críticos.

2022Master thesis

Open access

Exosomes and microvesicles in kidney transplantation: the long road from trash to gold

Publication . Ramalhete, Luís; Araújo, Rúben; Ferreira, Aníbal; Calado, Cecília

Kidney transplantation significantly enhances the survival rate and quality of life of patients with end-stage kidney disease. The ability to predict post-transplantation rejection events in their early phases can reduce subsequent allograft loss. Therefore, it is critical to identify biomarkers of rejection processes that can be acquired on routine analysis of samples collected by non-invasive or minimally invasive procedures. It is also important to develop new therapeutic strategies that facilitate optimisation of the dose of immunotherapeutic drugs and the induction of allograft immunotolerance. This review explores the challenges and opportunities offered by extracellular vesicles (EVs) present in biofluids in the discovery of biomarkers of rejection processes, as drug carriers and in the induction of immunotolerance. Since EVs are highly complex structures and their composition is affected by the parent cell's metabolic status, the importance of defining standardised methods for isolating and characterising EVs is also discussed. Understanding the major bottlenecks associated with all these areas will promote the further investigation of EVs and their translation into a clinical setting. .

2024Journal article

Open access

Label-free discrimination of T and B lymphocyte activation based on vibrational spectroscopy – A machine learning approach

Publication . Ramalhete, Luís; Araújo, Rúben; Ferreira, Aníbal; Calado, Cecília

B and T-lymphocytes are major players of the specific immune system, responsible by an efficient response to target antigens. Despite the high relevance of these cells’ activation in diverse human pathophysiological pro cesses, its analysis in clinical context presents diverse constraints. In the present work, MIR spectroscopy was used to acquire the cells molecular profile in a label-free, simple, rapid, economic, and high-throughput mode. Recurring to machine learning algorithms MIR data was subsequently evaluated. Models were developed based on specific spectral bands as selected by Gini index and the Fast Correlation Based Filter. To determine if it was, possible to predict from the spectra, if B and T lymphocyte were activated, and what was the molecular fingerprint of T- or B- lymphocyte activation. The molecular composition of activated lymphocytes was so different from naïve cells, that very good pre diction models were developed with whole spectra (with AUC=0.98). Activated B lymphocytes also present a very distinct molecular profile in relation to activated T lymphocytes, leading to excellent prediction models, especially if based on target bands (AUC=0.99). The identification of critical target bands, according to the metabolic differences between B and T lymphocytes and in association with the molecular mechanism of the activation process highlighted bands associated to lipids and glycogen levels. The method developed presents therefore, appealing characteristics to promote a new diagnostic tool to analyze and discriminate B from T-lymphocytes

2023Journal article

Open access