Cellular composition
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Cellular Composition: Insights from Diverse Research Studies
Evolutionary Tradeoffs in Bacterial Cellular Composition
Understanding the cellular composition of bacteria is crucial for comprehending their physiological functions and metabolic processes. Research has shown that bacterial cellular composition varies significantly with cell volume, affecting genomic, protein, cellular envelope, RNA, and ribosomal content. Notably, protein content trends are more complex than a simple proportionality with genome size, and ribosome numbers are dictated by biosynthesis needs across species1. Additionally, physical space constraints limit the size of bacteria, with smaller bacteria dominated by DNA and protein content, while larger bacteria face limitations due to ribosome volume requirements1. These findings highlight the intricate balance between cellular components and the basic energetics of cells, with metabolic rates scaling superlinearly with cellular components1.
Membrane Lipid Composition and Cellular Function
The composition of membrane lipids, including fatty acids, phospholipids, and cholesterol, plays a pivotal role in cellular function. Modifications in membrane lipid composition can alter membrane fluidity and impact various cellular processes such as carrier-mediated transport, enzyme properties, receptor binding, and cell growth2. These effects are complex and vary across different cell types, making it challenging to predict responses to lipid modifications. Changes in membrane lipid structure can directly influence the conformation and activity of transporters, receptors, and enzymes, thereby affecting cellular functions2.
Predicting Cellular Composition in Histology Images
In computational pathology, predicting cellular composition from histology images is essential for analyzing cellular topology, interactions, and disease outcomes. The ALBRT method has been developed to accurately predict the presence and counts of different cell types in tumor microenvironments from digitized Hematoxylin and Eosin (H&E) stained tissue sections. This method uses a contrastive-learning inspired design to learn compact and rotation-invariant feature representations, significantly improving cell classification and counting accuracy3. ALBRT's feature representation is transferrable to novel datasets and can be utilized for various downstream prediction tasks in computational pathology3.
Cellular Composition in Developmental Systems
Evaluating cellular composition during developmental stages can be achieved using the discrete Carson transformation. This method attaches an operator to each element of the generating word, allowing for the overall operator to be derived from the system's tree graph. This approach provides a systematic way to analyze cellular composition at different developmental stages4.
Deconvolution of Bulk Tissue Cell Type Composition
Characterizing cell type composition in disease-relevant tissues is crucial for identifying cellular targets of disease. The MuSiC method leverages single-cell RNA sequencing data to deconvolute bulk RNA-seq data, enabling the characterization of cell type compositions in complex tissues. This method outperforms existing approaches, particularly in tissues with closely related cell types, and facilitates the understanding of cellular heterogeneity in disease mechanisms5. By utilizing bulk tissue RNA-seq data, MuSiC allows for the elucidation of cell type contributions in disease, making it a valuable tool for genomic studies5.
Impact of Tissue Composition on Gene Co-Expression
Variable cellular composition in tissue samples poses challenges for interpreting genomic profiling studies. Differences in cell type composition can affect correlation-based network estimation and gene co-expression patterns. Deconvolution methods, initially developed for separating tumor and stromal signatures, can be applied to mixed cell-type tissues to improve the accuracy of co-expression estimates. These methods are particularly effective when using uncorrelated cell-type-specific markers6.
Cardiac Cellular Composition
Accurate knowledge of cardiac cellular composition is essential for understanding heart function and pathology. Recent studies have shown that endothelial cells constitute the majority of nonmyocytes in the heart, while fibroblasts are less abundant than previously thought. This new perspective emphasizes the significant role of endothelial cells in cardiac physiology and response to injury9 10. Improved techniques and markers have provided more accurate estimates of cell populations, highlighting the importance of endothelial cells in maintaining heart health and contributing to disease processes9 10.
Conclusion
The study of cellular composition across different biological contexts reveals the complexity and variability of cellular components and their functions. From bacterial physiology to human cardiac health, understanding cellular composition is crucial for advancing our knowledge of biological systems and developing targeted therapeutic strategies. The integration of advanced computational methods and experimental techniques continues to enhance our ability to characterize and interpret cellular composition in diverse tissues and organisms.
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Evolutionary tradeoffs in cellular composition across diverse bacteria
Cellular composition changes across diverse bacteria, with protein content trends more complex than genome size, and ribosome number influenced by biosynthesis requirements, limiting cell size at the largest and smallest sizes.
Rigorous JournalMembrane lipid composition and cellular function.
Membrane lipid composition modifications can alter cellular functions and are complex, with effects varying from cell to cell and system to system.
Highly CitedALBRT: Cellular Composition Prediction in Routine Histology Images
ALBRT accurately predicts the presence and counts of different cell types in a given histology image, offering significant improvement over existing state-of-the-art approaches for cell classification and counting.
On some models for developmental systems. Part III. Another approach for evaluating the cellular composition
The discrete Carson transformation can effectively evaluate the cellular composition of developmental systems at each stage, providing a tree graph-based approach for analyzing the system's evolution.
Bulk tissue cell type deconvolution with multi-subject single-cell expression reference
MuSiC effectively characterizes cell type compositions in complex tissues using single-cell RNA sequencing data, improving our understanding of disease mechanisms and cellular heterogeneity.
Highly CitedThe effect of tissue composition on gene co-expression
Variable cell-type composition in tissue samples significantly affects gene co-expression estimates, and using uncorrelated cell-type-specific markers can help deconvolute expression and co-expression patterns.
Rigorous JournalMETHOD FOR CALCULATING THE COMPOSITION OF CELLULAR CONCRETE
This study developed a method for calculating cellular concrete compositions based on experimental-statistical models, ensuring required strength and average density while minimizing raw material costs.
Compositional Control of Phase-Separated Cellular Bodies
Cellular body composition can be rapidly controlled by changes in scaffold concentration or valency, suggesting potential applications in controlling the composition of PML nuclear bodies and P bodies.
Highly CitedRevisiting Cardiac Cellular Composition.
Endothelial cells constitute the majority of noncardiomyocytes in the heart, playing a greater role in physiological function and injury response than previously appreciated.
In Vitro StudyHighly CitedRecounting Cardiac Cellular Composition.
The modern heart is composed of cardiomyocytes, fibroblasts, endothelial cells, and perivascular cells, with fibroblasts playing a crucial role in maintaining heart health and disease.
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