Physiological State Of Cells Research Articles

Establishment of closed 35S ribosomal RNA gene chromatin in stationary Saccharomyces cerevisiae cells.

As a first step in eukaryotic ribosome biogenesis RNA polymerase (Pol) I synthesizes a large ribosomal RNA (rRNA) precursor from multicopy rRNA gene loci. This process is essential for cellular growth and regulated in response to the cell's physiological state. rRNA gene transcription is downregulated upon growth to stationary phase in the yeast Saccharomyces cerevisiae. This reduction correlates with characteristic changes in rRNA gene chromatin structure from a transcriptionally active 'open' state to a non-transcribed 'closed' state. The conserved lysine deacetylase Rpd3 was shown to be required for this chromatin transition. We found that Rpd3 is needed for tight repression of Pol I transcription upon growth to stationary phase as a prerequisite for the establishment of the closed chromatin state. We provide evidence that Rpd3 regulates Pol I transcription by adjusting cellular levels of the Pol I preinitiation complex component core factor (CF). Importantly, our study identifies CF as the complex limiting the number of open rRNA genes in exponentially growing and stationary cells.

Super-Resolution Microscopic Imaging of Lipid Droplets in Living Cells via Carbonized Polymer Dot-Based Polarity-Responsive Nanoprobe.

Lipid droplets (LDs) are dynamic subcellular organelles that participate in various physiological processes, and their abnormality can also lead to various diseases. Tracing the dynamics of LDs in living cells will be valuable for understanding cell physiological states. Here, we employed a structured light illumination super-resolution imaging assisted with a carbonized polymer dot (CPD)-based fluorescence nanoprobe to track the travel paths of LDs and other organelles. The CPDs we developed are highly biocompatible with living cells and exhibit a highly sensitive response to solvent polarity, allowing for high specificity in staining LDs in living cells. Aided by these nanoprobes, we successfully observed many real-time LD-involved dynamics in living cells, such as intracellular LD interactions, communications with other organelles, and dynamic behaviors under external stimuli (oxidative stress inducer). These studies deepen our comprehension of the physiological role of LDs and drive the advancement of super-resolution fluorescent probes.

Exosome- Machine Learning Integration in Biomedicine: Advancing Diagnosis and Biomarker Discovery.

Exosomes, small extracellular vesicles (sEVs) secreted by various cell types, play crucial roles in intercellular communication and are increasingly recognized as valuable biomarkers for disease diagnosis and therapeutic targets. Meanwhile, machine learning (ML) techniques have revolutionized biomedical research by enabling the analysis of complex datasets and highly accurate prediction of disease outcomes. Exosomes, with their diverse cargo of proteins, nucleic acids, and lipids, offer a rich source of molecular information reflecting the physiological state of cells. Integrating exosome analysis with ML algorithms, including supervised and unsupervised learning techniques, allows for identifying disease-specific biomarkers and predicting disease outcomes based on exosome profiles. Integrating exosome biology with ML presents a promising avenue for advancing biomedical research and clinical practice. This review explores the intersection of exosome biology and ML in biomedicine, highlighting the importance of integrating these disciplines to advance our understanding of disease mechanisms and biomarker discovery.

Anomaly detection for high-content image-based phenotypic cell profiling.

High-content image-based phenotypic profiling combines automated microscopy and analysis to identify phenotypic alterations in cell morphology and provide insight into the cell's physiological state. Classical representations of the phenotypic profile can not capture the full underlying complexity in cell organization, while recent weakly machine-learning based representation-learning methods are hard to biologically interpret. We used the abundance of control wells to learn the in-distribution of control experiments and use it to formulate a self-supervised reconstruction anomaly-based representation that encodes the intricate morphological inter-feature dependencies while preserving the representation interpretability. The performance of our anomaly-based representations was evaluated for downstream tasks with respect to two classical representations across four public Cell Painting datasets. Anomaly-based representations improved reproducibility, Mechanism of Action classification, and complemented classical representations. Unsupervised explainability of autoencoder-based anomalies identified specific inter-feature dependencies causing anomalies. The general concept of anomaly-based representations can be adapted to other applications in cell biology.

Tris (2-chloroethyl) phosphate presence inhibits methane production from anaerobic digestion: Alterations in organic matter transformation, cell physiological status, and microbial community

Organophosphate flame retardants (OPFRs) are widely used in consumer products, leading to their unavoidable release into the environment, especially accumulation in anaerobic environments and posing potential risks. This study focused on Tris(2-chloroethyl) phosphate (TCEP), a representative OPFR, to investigate its effects on carbon transformation and methane production in anaerobic digestion. Increasing TCEP concentrations from control to 16 mg/L resulted in decreased cumulative methane yield (from 235.4 to 196.3 mL/g COD) and maximum daily methane yield (from 40.8 to 16.17 mL/(g COD·d)), along with an extended optimal anaerobic digestion time (from 15 to 20 days). Mechanistic analysis revealed TCEP binding to tyrosine-like proteins in extracellular polymeric substances, causing cell membrane integrity impairment. The TCEP-caused alteration of the physiological status of cells was demonstrated to be a significant contribution to the inhibited bioprocesses including acidogenesis, acetogenesis, and methanogenesis. Illumina Miseq sequencing showed TCEP decreasing the relative abundance of acidogens (58.8 % to 46.0 %) and acetogens (7.1 % to 5.0 %), partly shifting the methanogenesis pathway from acetoclastic to hydrogenotrophic methanogenesis. These findings enhance understanding of TCEP's impact on anaerobic digestion, emphasizing the environmental risk associated with its continued accumulation.

Comparison of Various Extraction Approaches for Optimized Preparation of Intracellular Metabolites from Human Mesenchymal Stem Cells and Fibroblasts for NMR-Based Study.

Metabolomics has proven to be a sensitive tool for monitoring biochemical processes in cell culture. It enables multi-analysis, clarifying the correlation between numerous metabolic pathways. Together with other analysis, it thus provides a global view of a cell's physiological state. A comprehensive analysis of molecular changes is also required in the case of mesenchymal stem cells (MSCs), which currently represent an essential portion of cells used in regenerative medicine. Reproducibility and correct measurement are closely connected to careful metabolite extraction, and sample preparation is always a critical point. Our study aimed to compare the efficiencies of four harvesting and six extraction methods. Several organic reagents (methanol, ethanol, acetonitrile, methanol-chloroform, MTBE) and harvesting approaches (trypsinization vs. scraping) were tested. We used untargeted nuclear magnetic resonance spectroscopy (NMR) to determine the most efficient method for the extraction of metabolites from human adherent cells, specifically human dermal fibroblasts adult (HDFa) and dental pulp stem cells (DPSCs). A comprehensive dataset of 29 identified and quantified metabolites were determined to possess statistically significant differences in the abundances of several metabolites when the cells were detached mechanically to organic solvent compared to when applying enzymes mainly in the classes of amino acids and peptides for both types of cells. Direct scraping to organic solvent is a method that yields higher abundances of determined metabolites. Extraction with the use of different polar reagents, 50% and 80% methanol, or acetonitrile, mostly showed the same quality. For both HDFa and DPSC cells, the MTBE method, methanol-chloroform, and 80% ethanol extractions showed higher extraction efficiency for the most identified and quantified metabolites Thus, preparation procedures provided a cell sample processing protocol that focuses on maximizing extraction yield. Our approach may be useful for large-scale comparative metabolomic studies of human mesenchymal stem cell samples.

Maximum entropy determination of mammalian proteome dynamics

Full understanding of proteostasis and energy utilization in cells will require knowledge of the fraction of cell proteins being degraded with different half-lives and their rates of synthesis. We therefore developed a method to determine such information that combines mathematical analysis of protein degradation kinetics obtained in pulse-chase experiments with Bayesian data fitting using the maximum entropy principle. This approach will enable rapid analyses of whole-cell protein dynamics in different cell types, physiological states, and neurodegenerative disease. Using it, we obtained surprising insights about protein stabilities in cultured cells normally and upon activation of proteolysis by mTOR inhibition and increasing cAMP or cGMP. It revealed that >90% of protein content in dividing mammalian cell lines is long-lived, with half-lives of 24 to 200 h, and therefore comprises much of the proteins in daughter cells. The well-studied short-lived proteins (half-lives < 10 h) together comprise <2% of cell protein mass, but surprisingly account for 10 to 20% of measurable newly synthesized protein mass. Evolution thus appears to have minimized intracellular proteolysis except to rapidly eliminate misfolded and regulatory proteins.

QTLs and Genes for Salt Stress Tolerance: A Journey from Seed to Seed Continued.

Rice (Oryza sativa L.) is a crucial crop contributing to global food security; however, its production is susceptible to salinity, a significant abiotic stressor that negatively impacts plant germination, vigour, and yield, degrading crop production. Due to the presence of exchangeable sodium ions (Na+), the affected plants sustain two-way damage resulting in initial osmotic stress and subsequent ion toxicity in the plants, which alters the cell's ionic homeostasis and physiological status. To adapt to salt stress, plants sense and transfer osmotic and ionic signals into their respective cells, which results in alterations of their cellular properties. No specific Na+ sensor or receptor has been identified in plants for salt stress other than the SOS pathway. Increasing productivity under salt-affected soils necessitates conventional breeding supplemented with biotechnological interventions. However, knowledge of the genetic basis of salinity stress tolerance in the breeding pool is somewhat limited because of the complicated architecture of salinity stress tolerance, which needs to be expanded to create salt-tolerant variants with better adaptability. A comprehensive study that emphasizes the QTLs, genes and governing mechanisms for salt stress tolerance is discussed in the present study for future research in crop improvement.

Gene Expression Tradeoffs Determine Bacterial Survival and Adaptation to Antibiotic Stress.

To optimize their fitness, cells face the crucial task of efficiently responding to various stresses. This necessitates striking a balance between conserving resources for survival and allocating resources for growth and division. The fundamental principles governing these tradeoffs is an outstanding challenge in the physics of living systems. In this study, we introduce a coarse-grained theoretical framework for bacterial physiology that establishes a connection between the physiological state of cells and their survival outcomes in dynamic environments, particularly in the context of antibiotic exposure. Predicting bacterial survival responses to varying antibiotic doses proves challenging due to the profound influence of the physiological state on critical parameters, such as the minimum inhibitory concentration (MIC) and killing rates, even within an isogenic cell population. Our proposed theoretical model bridges the gap by linking extracellular antibiotic concentration and nutrient quality to intracellular damage accumulation and gene expression. This framework allows us to predict and explain the control of cellular growth rate, death rate, MIC, and survival fraction in a wide range of time-varying environments. Surprisingly, our model reveals that cell death is rarely due to antibiotic levels being above the maximum physiological limit, but instead survival is limited by the inability to alter gene expression sufficiently quickly to transition to a less susceptible physiological state. Moreover, bacteria tend to overexpress stress response genes at the expense of reduced growth, conferring greater protection against further antibiotic exposure. This strategy is in contrast to those employed in different nutrient environments, in which bacteria allocate resources to maximize growth rate. This highlights an important tradeoff between the cellular capacity for growth and the ability to survive antibiotic exposure.

Biotransformation of maize bran-derived ferulic acid to vanillin using an adapted strain of Amycolatopsis sp. ATCC 39116.

Maize bran, an agro-processing waste residue, is a good source of ferulic acid that can be further valorized for vanillin production. However, extraction of ferulic acid from natural sources has been challenging due to low concentrations and intensive extraction procedures. In the present work, ferulic acid streams (purities ranging from 5% to 75%) extracted from maize bran using thermochemical methods were evaluated for biotransformation to vanillin, employing Amycolatopsis sp. as a whole-cell biocatalyst. Initial adaptation studies were critical in improving ferulic acid assimilation and its conversion to vanillin by 65% and 56%, respectively by the fourth adaptation cycle. The effect of cell's physiological states and vanillic acid supplementation on vanillin production was studied using standard ferulic acid as a substrate in an effort to achieve further improvement in vanillin yield. In the presence of vanillic acid, 18 h cultured cells using 2 g/L of standard and isolated ferulic acid produced vanillin concentrations of up to 0.71 and 0.48 g/L, respectively. Furthermore, intermediates involved in the ferulic acid catabolic pathway and their interrelations were studied using GC-MS analysis. Results indicated that two different routes were involved in the catabolism of standard ferulic acid, and similar metabolic routes were observed for an isolated ferulic acid stream. These findings effectively evaluated isolated ferulic acid for sustainable vanillin production while reducing agro-industrial waste pollution.

Differential Expression of Stress Adaptation Genes in a Diatom Ulnaria acus under Different Culture Conditions.

Diatoms are a group of unicellular eukaryotes that are essential primary producers in aquatic ecosystems. The dynamic nature of their habitat necessitates a quick and specific response to various stresses. However, the molecular mechanisms of their physiological adaptations are still underexplored. In this work, we study the response of the cosmopolitan freshwater diatom Ulnaria acus (Bacillariophyceae, Fragilariophycidae, Licmophorales, Ulnariaceae, Ulnaria) in relation to a range of stress factors, namely silica deficiency, prolonged cultivation, and interaction with an algicidal bacterium. Fluorescent staining and light microscopy were used to determine the physiological state of cells under these stresses. To explore molecular reactions, we studied the genes involved in the stress response-type III metacaspase (MC), metacaspase-like proteases (MCP), death-specific protein (DSP), delta-1-pyrroline-5-carboxylate dehydrogenase (ALDH12), and glutathione synthetase (GSHS). We have described the structure of these genes, analyzed the predicted amino acid sequences, and measured their expression dynamics in vitro using qRT-PCR. We demonstrated that the expression of UaMC1, UaMC3, and UaDSP increased during the first five days of silicon starvation. On the seventh day, it was replaced with the expression of UaMC2, UaGSHS, and UaALDH. After 45 days of culture, cells stopped growing, and the expression of UaMC1, UaMC2, UaGSHS, and UaDSP increased. Exposure to an algicidal bacterial filtrate induced a higher expression of UaMC1 and UaGSHS. Thus, we can conclude that these proteins are involved in diatoms' adaptions to environmental changes. Further, these data show that the molecular adaptation mechanisms in diatoms depend on the nature and exposure duration of a stress factor.

Extracellular vesicles involved in growth regulation and metabolic modulation in Haematococcus pluvialis

BackgroundMicroalgae-derived extracellular vesicles (EVs), which transfer their cargos to the extracellular environment to affect recipient cells, play important roles in microalgal growth and environmental adaptation. And, they are also considered as sustainable and renewable bioresources of delivery nanocarrier for bioactive molecules and/or artificial drug molecules. However, their molecular composition and functions remain poorly understood.ResultsIn this study, isolation, characterization, and functional verification of Haematococcus pluvialis-derived EVs (HpEVs) were performed. The results indicated that HpEVs with typical EV morphology and size were secreted by H. pluvialis cells during the whole period of growth and accumulated in the culture medium. Cellular uptake of HpEVs by H. pluvialis was confirmed, and their roles in regulation of growth and various physiological processes of the recipient cells were also characterized. The short-term inhibition of HpEV secretion results in the accumulation of functional cellular components of HpEVs, thereby altering the biological response of these cells at the molecular level. Meanwhile, continuously inhibiting the secretion of HpEVs negatively influenced growth, and fatty acid and astaxanthin accumulation in H. pluvialis. Small RNA high-throughput sequencing was further performed to determine the miRNA cargoes and compelling details in HpEVs in depth. Comparative analysis revealed commonalities and differences in miRNA species and expression levels in three stages of HpEVs. A total of 163 mature miRNAs were identified with a few unique miRNAs reveal the highest expression levels, and miRNA expression profile of the HpEVs exhibited a clear stage-specific pattern. Moreover, a total of 12 differentially expressed miRNAs were identified and their target genes were classified to cell cycle control, lipid transport and metabolism, secondary metabolites biosynthesis and so on.ConclusionIt was therefore proposed that cargos of HpEVs, including miRNA constituents, were suggested potential roles in modulate cell physiological state of H. pluvialis. To summarize, this work uncovers the intercellular communication and metabolism regulation functions of HpEVs.

Measuring Vibrational Modes in Living Human Cells

The question of whether human living cells in physiological conditions exhibit mechanical resonances has remained unresolved for more than 70 years. We here construct a theoretical model to predict how these hypothetical vibration modes of a living cell can modify the stochastic response of a supporting micromechanical resonator. The key signatures that demonstrate the existence of these vibration modes are then searched for experimentally. To this aim, single human breast epithelial cells were attached to the surface of compliant small microcantilevers. Close examination of the frequency spectra of the thermal fluctuations of the microcantilever over a high-frequency range of 1 kHz–1 MHz revealed anomalies that are consistent with our theoretical predictions. We identify the stochastic coupling of the first and third vibration modes of the cell with the torsional and flexural vibration modes of the cantilever, respectively. These mechanical resonances are very broad and are located at 10–30 kHz and 150–180 kHz, respectively. The analysis of the experiments allows us to obtain information on the temporal evolution of several physical properties of the cell, in addition to the resonance frequencies, during the adhesion process—such as the mass, viscoelasticity, and contact area. These results open multiple avenues in our understanding of single cell mechanobiology, vibrational spectrometry of living cells and mechanical destruction of cancerous cells by targeting specific cell frequencies. Published by the American Physical Society 2024

Ratiometric NIR cell membrane-targeted probe for monitoring cell membrane polarity and tumor application

The investigation of cell membrane polarity is critical to determining cell physiological states and related disease for diagnosis and treatment. Herein, a ratiometric NIR cell membrane-targeted fluorescent probe, Cy-C12-MpN, was designed and synthesized to assess cell membrane polarity, which has an amphiphilic structure consisting of a dodecyl unit and a quaternary ammonium unit. It exhibited changes in emission wavelength and fluorescence intensity with the changes in polarity of the solution. It can use the ratio signal of the two peaks to report the polarity difference in its environment. The self-tuning of the probe internal system ensures the reliability of the test results and can be a semi-quantitative detection method. Indeed, in vitro polarity test, the ratio signal reported by Cy-C12-MpN had an optimal linear range with respect to polarity. Cell imaging illustrated the excellent cell membrane-targeted ability of Cy-C12-MpN, as it can rapidly target the cell membrane within 30 s and still anchor at it well after 1 hr. Moreover, Cy-C12-MpN showed that cell membrane polarity decreases after cells become cancerous, and observed the changes in cell membrane polarity caused by homeostatic membrane response stimulated by polyunsaturated fatty acids firstly. Imaging in vivo demonstrated the ability of Cy-C12-MpN to distinguish between tumor cells and normal cells, and further demonstrated that tumor cells have lower cell membrane polarity. In summary, the accuracy of the ratio detection of the probe Cy-C12-MpN and its excellent cell membrane targeting ability reveal reliable results that can provide very useful insights for cancer research.

Development and applications of genome-scale metabolic network models.

The genome-scale metabolic network model is an effective tool for characterizing the gene-protein-response relationship in the entire metabolic pathway of an organism. By combining various algorithms, the genome-scale metabolic network model can effectively simulate the influence of a specific environment on the physiological state of cells, optimize the culture conditions of strains, and predict the targets of genetic modification to achieve targeted modification of strains. In this review, we summarize the whole process of model building, sort out the various tools that may be involved in the model building process, and explain the role of various algorithms in model analysis. In addition, we also summarized the application of GSMM in network characteristics, cell phenotypes, metabolic engineering, etc. Finally, we discuss the current challenges facing GSMM.

G-Quadruplexes as Sensors of Intracellular Na+/K+ Ratio: Potential Role in Regulation of Transcription and Translation.

Data on the structure of G-quadruplexes, noncanonical nucleic acid forms, supporting an idea of their potential participation in regulation of gene expression in response to the change in intracellular Na+i/K+i ratio are considered in the review. Structural variety of G-quadruplexes, role of monovalent cations in formation of this structure, and thermodynamic stability of G-quadruplexes are described. Data on the methods of their identification in the cells and biological functions of these structures are presented. Analysis of information about specific interactions of G-quadruplexes with some proteins was conducted, and their potential participation in the development of some pathological conditions, in particular, cancer and neurodegenerative diseases, is considered. Special attention is given to the plausible role of G-quadruplexes as sensors of intracellular Na+i/K+i ratio, because alteration of this parameter affects folding of G-quadruplexes changing their stability and, thereby, organization of the regulatory elements of nucleic acids. The data presented in the conclusion section demonstrate significant change in the expression of some early response genes under certain physiological conditions of cells and tissues depending on the intracellular Na+i/K+i ratio.

A microfluidic microalgae detection system for cellular physiological response based on an object detection algorithm.

The composition of species and the physiological status of microalgal cells serve as significant indicators for monitoring marine environments. Symbiotic with corals, Symbiodiniaceae are more sensitive to the environmental response. However, current methods for evaluating microalgae tend to be population-based indicators that cannot be focused on single-cell level, ignoring potentially heterogeneous cells as well as cell state transitions. In this study, we proposed a microalgal cell detection method based on computer vision and microfluidics, which combined microscopic image processing, microfluidic chip and convolutional neural network to achieve label-free, sheathless, automated and high-throughput microalgae identification and cell state assessment. By optimizing the data import, training process and model architecture, we solved the problem of identifying tiny objects at the micron scale, and the optimized model was able to perform the tasks of cell multi-classification and physiological state assessment with more than 95% mean average precision. We discovered a novel transition state and explored the thermal sensitivity of three clades of Symbiodiniaceae, and discovered the phenomenon of cellular heat shock at high temperatures. The evolution of the physiological state of Symbiodiniaceae cells is very important for directional cell evolution and early warning of coral ecosystem health.

Recent Progress in Mass Spectrometry-Based Single-Cell Metabolic Analysis.

Single-cell analysis enables the measurement of biomolecules at the level of individual cells, facilitating in-depth investigations into cellular heterogeneity and precise interpretation of the related biological mechanisms. Among these biomolecules, cellular metabolites exhibit remarkable sensitivity to environmental and biochemical changes, unveiling a hidden world underlying cellular heterogeneity and allowing for the determination of cell physiological states. However, the metabolic analysis of single cells is challenging due to the extremely low concentrations, substantial content variations, and rapid turnover rates of cellular metabolites. Mass spectrometry (MS), characterized by its high sensitivity, wide dynamic range, and excellent selectivity, is employed in single-cell metabolic analysis. This review focuses on recent advances and applications of MS-based single-cell metabolic analysis, encompassing three key steps of single-cell isolation, detection, and application. It is anticipated that MS will bring profound implications in biomedical practices, serving as advanced tools to depict the single-cell metabolic landscape.

A deformability-based biochip for precise label-free stratification of metastatic subtypes using deep learning

Cellular deformability is a promising biomarker for evaluating the physiological state of cells in medical applications. Microfluidics has emerged as a powerful technique for measuring cellular deformability. However, existing microfluidic-based assays for measuring cellular deformability rely heavily on image analysis, which can limit their scalability for high-throughput applications. Here, we develop a parallel constriction-based microfluidic flow cytometry device and an integrated computational framework (ATMQcD). The ATMQcD framework includes automatic training set generation, multiple object tracking, segmentation, and cellular deformability quantification. The system was validated using cancer cell lines of varying metastatic potential, achieving a classification accuracy of 92.4% for invasiveness assessment and stratifying cancer cells before and after hypoxia treatment. The ATMQcD system also demonstrated excellent performance in distinguishing cancer cells from leukocytes (accuracy = 89.5%). We developed a mechanical model based on power-law rheology to quantify stiffness, which was fitted with measured data directly. The model evaluated metastatic potentials for multiple cancer types and mixed cell populations, even under real-world clinical conditions. Our study presents a highly robust and transferable computational framework for multiobject tracking and deformation measurement tasks in microfluidics. We believe that this platform has the potential to pave the way for high-throughput analysis in clinical applications, providing a powerful tool for evaluating cellular deformability and assessing the physiological state of cells.

Colocalization of expression transcripts with COVID-19 outcomes is rare across cell states, cell types and organs.

Identifying causal genes at GWAS loci can help pinpoint targets for therapeutic interventions. Expression studies can disentangle such loci but signals from expression quantitative trait loci (eQTLs) often fail to colocalize-which means that the genetic control of measured expression is not shared with the genetic control of disease risk. This may be because gene expression is measured in the wrong cell type, physiological state, or organ. We tested whether Mendelian randomization (MR) could identify genes at loci influencing COVID-19 outcomes and whether the colocalization of genetic control of expression and COVID-19 outcomes was influenced by cell type, cell stimulation, and organ. We conducted MR of cis-eQTLs from single cell (scRNA-seq) and bulk RNA sequencing. We then tested variables that could influence colocalization, including cell type, cell stimulation, RNA sequencing modality, organ, symptoms of COVID-19, and SARS-CoV-2 status among individuals with symptoms of COVID-19. The outcomes used to test colocalization were COVID-19 severity and susceptibility as assessed in the Host Genetics Initiative release 7. Most transcripts identified using MR did not colocalize when tested across cell types, cell state and in different organs. Most that did colocalize likely represented false positives due to linkage disequilibrium. In general, colocalization was highly variable and at times inconsistent for the same transcript across cell type, cell stimulation and organ. While we identified factors that influenced colocalization for select transcripts, identifying 33 that mediate COVID-19 outcomes, our study suggests that colocalization of expression with COVID-19 outcomes is partially due to noisy signals even after following quality control and sensitivity testing. These findings illustrate the present difficulty of linking expression transcripts to disease outcomes and the need for skepticism when observing eQTL MR results, even accounting for cell types, stimulation state and different organs.