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- Label-free discrimination of T and B lymphocyte activation based on vibrational spectroscopy – A machine learning approachPublication . Ramalhete, Luís; Araújo, Rúben; Ferreira, Aníbal; Calado, CecíliaB 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
- Label-free discrimination of T and B lymphocyte activation based on vibrational spectroscopy: a machine learning approachPublication . Ramalhete, Luís; Araújo, Rúben; Ferreira, Aníbal; Calado, CecíliaB 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.
- Discriminating B and T-lymphocyte from its molecular profile acquired in a label-free and high-throughput methodPublication . Ramalhete, Luís; Araújo, Rúben; Calado, CecíliaB and T-lymphocytes are one of the main players of the adaptive immune system and consequently, the ability to discriminate these cells is relevant in diverse pathophysiological cases. In the present work, FTIR spectroscopy was used to acquire the cells molecular profile in a label-free, simple, rapid, economic, and high-throughput mode. It was possible, by hierarchical cluster analysis of the second derivative spectra, between 900 to 1500 cm-1, to correctly classify 98% of samples as B and T-lymphocyte. It was also possible to develop a very good support vector machine model, which could predict B and T-lymphocyte from the second derivative spectra, with high accuracy (0.95), precision (0.95), recall (0.95), and F1 score (0.95). The method developed presents therefore, appealing characteristics to promote a new diagnostic tool to analyze and discriminate B from T-lymphocytes.