Probing the distribution of pharmaceutical compounds in cells using ToF-SIMS

The primary objective of this thesis is to describe the work that I have undertaken during my PhD, evaluating the applicability of Time of Flight Secondary Ion Mass Spectrometry (ToF-SIMS) to study intracellular drug distribution. ToF-SIMS, as a technique has been widely used to characterize inorganic1 and organic materials, or even unexpected mixtures of both. My objective is to determine if that can be extended to drug discovery, by identifying pharmaceutical drugs in biological matrices such as cells. In drug discovery, intracellular drug distribution is a subject of profound interest, as scientists need to know if the drug is reaching the target of interest, or if is having adverse off-target activity. However, the techniques currently used to inform on this do not provide a definitive answer to the question, as either spatial resolution or molecular properties are compromised. For example, Matrix Assisted Laser Desorption Ionisation (MALDI) cannot achieve sub-cellular spatial resolution4 and even the most advance microscopy applications are dependent on labels that might compromise the physical-chemical properties of a drug molecule. ToF-SIMS bridges the gap, as it has improved spatial resolution and the drug molecules are presented to the in vitro or in vivo system label free with minimal sample preparation. SIMS is also capable of providing three-dimensional information and even potential metabolomics information when OrbiSIMS is employed3.In this study, I describe the first time a drug molecule, amiodarone, was visualized inside a mammalian cell using ToF-SIMS, this work was published in 2015. I then extend that methodology to other cell lines, and compare the ToF-SIMS data with Liquid-Chromatography tandem Mass Spectrometry data. This study also considers the variability found within cell populations and how different cells have a range of amiodarone intensities, and therefore exhibit different incorporation rates despite being derived from the same clones; this work was published in 2017. This thesis then focuses on the applicability of ToF-SIMS to the analysis of other drug molecules and investigates if a compound’s physical-chemical properties can provide any indication of the ionisation efficiency of a pharmaceutical drug in ToF-SIMS, this work is currently being written for a peer reviewed publication. The last chapter concentrates on bacterial imaging using both ToF and Orbi SIMS; the former allows the visualization of a drug molecule inside the bacteria of interest and the latter allows lower resolution imaging but extraction of metabolomic information as a result of the high mass resolution, high mass accuracy data generated by the Orbitrap mass analyser

Probing the distribution of pharmaceutical compounds in cells using ToF-SIMS

The primary objective of this thesis is to describe the work that I have undertaken during my PhD, evaluating the applicability of Time of Flight Secondary Ion Mass Spectrometry (ToF-SIMS) to study intracellular drug distribution. ToF-SIMS, as a technique has been widely used to characterize inorganic1 and organic materials, or even unexpected mixtures of both. My objective is to determine if that can be extended to drug discovery, by identifying pharmaceutical drugs in biological matrices such as cells. In drug discovery, intracellular drug distribution is a subject of profound interest, as scientists need to know if the drug is reaching the target of interest, or if is having adverse off-target activity. However, the techniques currently used to inform on this do not provide a definitive answer to the question, as either spatial resolution or molecular properties are compromised. For example, Matrix Assisted Laser Desorption Ionisation (MALDI) cannot achieve sub-cellular spatial resolution4 and even the most advance microscopy applications are dependent on labels that might compromise the physical-chemical properties of a drug molecule. ToF-SIMS bridges the gap, as it has improved spatial resolution and the drug molecules are presented to the in vitro or in vivo system label free with minimal sample preparation. SIMS is also capable of providing three-dimensional information and even potential metabolomics information when OrbiSIMS is employed3.In this study, I describe the first time a drug molecule, amiodarone, was visualized inside a mammalian cell using ToF-SIMS, this work was published in 2015. I then extend that methodology to other cell lines, and compare the ToF-SIMS data with Liquid-Chromatography tandem Mass Spectrometry data. This study also considers the variability found within cell populations and how different cells have a range of amiodarone intensities, and therefore exhibit different incorporation rates despite being derived from the same clones; this work was published in 2017. This thesis then focuses on the applicability of ToF-SIMS to the analysis of other drug molecules and investigates if a compound’s physical-chemical properties can provide any indication of the ionisation efficiency of a pharmaceutical drug in ToF-SIMS, this work is currently being written for a peer reviewed publication. The last chapter concentrates on bacterial imaging using both ToF and Orbi SIMS; the former allows the visualization of a drug molecule inside the bacteria of interest and the latter allows lower resolution imaging but extraction of metabolomic information as a result of the high mass resolution, high mass accuracy data generated by the Orbitrap mass analyser

Whole-genome sequencing reveals host factors underlying critical COVID-19

Altres ajuts: Department of Health and Social Care (DHSC); Illumina; LifeArc; Medical Research Council (MRC); UKRI; Sepsis Research (the Fiona Elizabeth Agnew Trust); the Intensive Care Society, Wellcome Trust Senior Research Fellowship (223164/Z/21/Z); BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070, BBS/E/D/30002275); UKRI grants (MC_PC_20004, MC_PC_19025, MC_PC_1905, MRNO2995X/1); UK Research and Innovation (MC_PC_20029); the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z); the Edinburgh Clinical Academic Track (ECAT) programme; the National Institute for Health Research, the Wellcome Trust; the MRC; Cancer Research UK; the DHSC; NHS England; the Smilow family; the National Center for Advancing Translational Sciences of the National Institutes of Health (CTSA award number UL1TR001878); the Perelman School of Medicine at the University of Pennsylvania; National Institute on Aging (NIA U01AG009740); the National Institute on Aging (RC2 AG036495, RC4 AG039029); the Common Fund of the Office of the Director of the National Institutes of Health; NCI; NHGRI; NHLBI; NIDA; NIMH; NINDS.Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care or hospitalization after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes-including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)-in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease