Biology subjects

Geladaki, A.

Publications and source records attributed to Geladaki, A..

3 recordsLinked to original sources

bioRxiv2026

High-throughput platforms for genetic perturbation screening using CRISPR/Cas9 in human iPSC-derived macrophages for drug discovery

Human induced Pluripotent Stem Cell (hiPSC) models have revolutionised drug discovery, offering high translational relevance for recapitulating disease biology and thereby the potential to help reduce drug attrition. Macrophages are pivotal for maintaining tissue homeostasis and orchestrating immune responses; their dysregulation underpins several diseases, including autoinflammatory disorders, neurodegeneration, and cancer. Therapeutically targeting this cell type presents an attractive strategy to simultaneously influence multiple cellular mediators and functions. We have established a scalable, semi-automated, and physiologically relevant hiPSC-derived macrophage model, rigorously characterised through deep comparative multi-omics. We have also integrated our hiPSC-derived macrophage platform with large-scale CRISPR screening workflows designed for parallel genetic interrogation of thousands of gene targets, in both arrayed and pooled formats. In this manuscript, we apply those genetic screening methods to hiPSC-derived macrophages and showcase how genetic perturbations alter pro- and anti-inflammatory transcriptional signatures and significantly impact functional phenotypes in this cell model. This integrated approach allows for the exploration of novel genetic insights linked to causal disease biology, advancing myeloid cell-associated target discovery across a broad spectrum of therapeutic areas.

immunology↗

bioRxiv2020

A semi-supervised Bayesian approach for simultaneous protein sub-cellular localisation assignment and novelty detection

The cell is compartmentalised into complex micro-environments allowing an array of specialised biological processes to be carried out in synchrony. Determining a proteins sub-cellular localisation to one or more of these compartments can therefore be a first step in determining its function. High-throughput and high-accuracy mass spectrometry-based sub-cellular proteomic methods can now shed light on the localisation of thousands of proteins at once. Machine learning algorithms are then typically employed to make protein-organelle assignments. However, these algorithms are limited by insufficient and incomplete annotation. We propose a semi-supervised Bayesian approach to novelty detection, allowing the discovery of additional, previously unannotated sub-cellular niches. Inference in our model is performed in a Bayesian framework, allowing us to quantify uncertainty in the allocation of proteins to new sub-cellular niches, as well as in the number of newly discovered compartments. We apply our approach across 10 mass spectrometry based spatial proteomic datasets, representing a diverse range of experimental protocols. Application of our approach to hyper LOPIT datasets validates its utility by recovering enrichment with chromatin-associated proteins without annotation and uncovers sub-nuclear compartmentalisation which was not identified in the original analysis. Moreover, using sub-cellular proteomics data from Saccharomyces cerevisiae, we uncover a novel group of proteins trafficking from the ER to the early Golgi apparatus. Overall, we demonstrate the potential for novelty detection to yield biologically relevant niches that are missed by current approaches.

systems biology↗

bioRxiv2018

LOPIT-DC: A simpler approach to high-resolution spatial proteomics

Hyperplexed Localisation of Organelle Proteins by Isotope Tagging (hyperLOPIT) is a well-established method for studying protein subcellular localisation in complex biological samples. As a simpler alternative we developed a second workflow named Localisation of Organelle Proteins by Isotope Tagging after Differential ultraCentrifugation (LOPIT-DC) which is faster and less resource-intensive. We present the most comprehensive high-resolution mass spectrometry-based human dataset to date and deliver a flexible set of subcellular proteomics protocols for sample preparation and data analysis. For the first time, we methodically compare these two different mass spectrometry-based spatial proteomics methods within the same study and also apply QSep, the first tool that objectively and robustly quantifies subcellular resolution in spatial proteomics data. Using both approaches we highlight suborganellar resolution and isoform-specific subcellular niches as well as the locations of large protein complexes and proteins involved in signalling pathways which play important roles in cancer and metabolism. Finally, we showcase an extensive analysis of the multilocalising proteome identified via both methods.

biochemistry↗