Biology subjects

Georgiev, D.

Publications and source records attributed to Georgiev, D..

4 recordsLinked to original sources

bioRxiv2025

Label-free biochemical imaging and timepoint analysis of neural organoids via deep learning-enhanced Raman microspectroscopy

Three-dimensional organoids have emerged as powerful models for studying human development, disease and drug response in vitro. Yet, their analysis remains constrained by standard imaging and characterisation techniques, which are invasive, require exogenous labelling and offer limited multiplexing. Here, we present a non-invasive, label-free imaging platform that integrates Raman microspectroscopy with deep learning-based hyperspectral unmixing for unsupervised, spatially resolved biochemical analysis of neural organoids. Our approach enables high-resolution mapping of cellular and subcellular structures in both cryosectioned and intact organoids, achieving improved imaging accuracy and robustness compared to conventional methods for hyperspectral analysis. Using our platform, we demonstrate volumetric imaging of a neural rosette within a neural organoid, and interrogate changes in biochemical composition during early developmental stages in intact neural organoids, revealing spatiotemporal variations in lipids, proteins and nucleic acids. This work establishes a versatile framework for high-content, label-free (bio)chemical phenotyping with broad applications in organoid research and beyond.

bioinformatics↗

bioRxiv2025

Context-Aware Synthetic Promoter Design Using Neural Networks Enables Rewiring of Eukaryotic Transcriptional Networks

Gene regulation through promoter engineering is a cornerstone of synthetic biology, enabling precise control over transcriptional networks. However, experimental approaches remain labor-intensive. While artificial neural networks (ANNs) have improved regulatory element prediction, tools for promoter-transcription factor binding site (TFBS) recombination are still lacking. We present an ANN framework for context-aware design of synthetic promoters in Saccharomyces cerevisiae. The model predicts optimal TFBS insertion sites and the extent of promoter rewriting needed for successful integration. Applying this, we screened 6,011 native yeast promoters for compatibility with the TetR TFBS, generating a ranked list of high-confidence promoter-TFBS pairs. Experimental validation showed that model-designed promoters achieved repression rates up to 98.4%, without prior experimental characterization or tuning. We further rewired the yeast transcriptional network by introducing glucose-dependent regulation of an essential gene via Mig1 TFBS insertion. These results establish a scalable, predictive method for engineering regulatory sequences and reprogramming transcriptional logic.

bioinformatics↗

bioRxiv2024

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↗

bioRxiv2023

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↗