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Biology subjects

Georgiev, D.

Publications and source records attributed to Georgiev, D..

2 recordsLinked to original sources

Massively parallel identification of single-cell immunophenotypes

Translating insights from single-cell analysis into actionable indicators of health and disease requires large-scale confirmatory studies. We introduce biocytometry, a novel method utilizing engineered bioparticles for multiparametric immunophenotyping in suspension, enabling simultaneous measurement across thousands of assays with single-cell sensitivity and a wide dynamic range (1 to 1,000 target cells/sample). The technical validation of biocytometry revealed strong alignment with established technologies (mean bias = 0.25%, LoA = -1.83% to 2.33%) for low-sensitivity settings. Biocytometry excelled in high-sensitivity settings, consistently showcasing superior sensitivity and specificity (LoB = 0), irrespective of the sample type. By employing multiparametric target cell identification, we harnessed the homogeneous assay workflow to discern cell-specific apoptosis in mixed cell cultures. Potential applications include monitoring rare premalignant subpopulations in indications such as smoldering multiple myeloma (SMM), enhancing the detection of circulating tumor cells (CTCs), advancing pharmacokinetic assessments in chimeric antigen receptor (CAR) T-cell therapies, and improving the accuracy of minimal residual disease (MRD) evaluations. Additionally, the high throughput and cell-specific readout capabilities might provide substantial value in drug development, especially for the analysis of complex sample matrices, such as primary cell cultures and organoids.

synthetic biology↗

RamanSPy: An open-source Python package for integrative Raman spectroscopy data analysis

Raman spectroscopy is a non-destructive and label-free chemical analysis technique, which plays a key role in the analysis and discovery cycle of various branches of science. Nonetheless, progress in Raman spectroscopic analysis is still impeded by the lack of software, methodological and data standardisation, and the ensuing fragmentation and lack of reproducibility of analysis workflows thereof. To address these issues, we introduce RamanSPy, an open-source Python package for Raman spectroscopic research and analysis. RamanSPy provides a comprehensive library of ready-to-use tools for spectroscopic analysis, which streamlines day-to-day tasks, integrative analyses, as well as novel research and algorithmic development. RamanSPy is modular and open source, not tied to a particular technology or data format, and can be readily interfaced with the burgeoning ecosystem for data science, statistical analysis and machine learning in Python.

bioinformatics↗