Integrative Biology最新文献

Clustering of cells is an essential component of many biological processes from tissue formation to cancer metastasis. We develop a minimal, Vicsek-based model of cellular interactions that robustly and accurately captures the variable propensity of different cells to form groups when confined. We calibrate and validate the model with experimental data on clustering affinities of four lines of tumor cells. We then show that cell clustering or separation tendencies are retained in environments with higher cell number densities and in cell mixtures. Finally, we calibrate our model with experimental measurements on the separation of cells treated with anti-clustering agents and find that treated cells maintain their distances in denser suspensions. We show that the model reconstructs several cell interaction mechanisms, which makes it suitable for exploring the dynamics of cell cluster formation as well as cell separation. Insight: We developed a model of cellular interactions that captures the clustering and separation of cells in an enclosure. Our model is particularly relevant for microfluidic systems with confined cells and we centered our work around one such emerging assay for the detection and research on clustering breast cancer cells. We calibrated our model using the existing experimental data and used it to explore the functionality of the assay under a broader set of conditions than originally considered. Future usages of our model can include purely theoretical and computational considerations, exploring experimental devices, and supporting research on small to medium-sized cell clusters.

The recurrence of cancer following chemotherapy treatment is a major cause of death across solid and hematologic cancers. In B-cell acute lymphoblastic leukemia (B-ALL), relapse after initial chemotherapy treatment leads to poor patient outcomes. Here we test the hypothesis that chemotherapy-treated versus control B-ALL cells can be characterized based on cellular physical phenotypes. To quantify physical phenotypes of chemotherapy-treated leukemia cells, we use cells derived from B-ALL patients that are treated for 7 days with a standard multidrug chemotherapy regimen of vincristine, dexamethasone, and L-asparaginase (VDL). We conduct physical phenotyping of VDL-treated versus control cells by tracking the sequential deformations of single cells as they flow through a series of micron-scale constrictions in a microfluidic device; we call this method Quantitative Cyclical Deformability Cytometry. Using automated image analysis, we extract time-dependent features of deforming cells including cell size and transit time (TT) with single-cell resolution. Our findings show that VDL-treated B-ALL cells have faster TTs and transit velocity than control cells, indicating that VDL-treated cells are more deformable. We then test how effectively physical phenotypes can predict the presence of VDL-treated cells in mixed populations of VDL-treated and control cells using machine learning approaches. We find that TT measurements across a series of sequential constrictions can enhance the classification accuracy of VDL-treated cells in mixed populations using a variety of classifiers. Our findings suggest the predictive power of cell physical phenotyping as a complementary prognostic tool to detect the presence of cells that survive chemotherapy treatment. Ultimately such complementary physical phenotyping approaches could guide treatment strategies and therapeutic interventions. Insight box Cancer cells that survive chemotherapy treatment are major contributors to patient relapse, but the ability to predict recurrence remains a challenge. Here we investigate the physical properties of leukemia cells that survive treatment with chemotherapy drugs by deforming individual cells through a series of micron-scale constrictions in a microfluidic channel. Our findings reveal that leukemia cells that survive chemotherapy treatment are more deformable than control cells. We further show that machine learning algorithms applied to physical phenotyping data can predict the presence of cells that survive chemotherapy treatment in a mixed population. Such an integrated approach using physical phenotyping and machine learning could be valuable to guide patient treatments.

Sensory pathways first elucidated in Caenorhabditis elegans are conserved across free-living and parasitic nematodes, even though each species responds to a diverse array of compounds. Most nematode sensory assays are performed by tallying observations of worm behavior on two-dimensional planes using agarose plates. These assays have been successful in the study of volatile sensation but are poorly suited for investigation of water-soluble gustation or parasitic nematodes without a free-living stage. In contrast, gustatory assays tend to be tedious, often limited to the manipulation of a single individual at a time. We have designed a nematode sensory assay using a microfluidics device that allows for the study of gustation in a 96-well, three-dimensional environment. This device is suited for free-living worms and parasitic worms that spend their lives in an aqueous environment, and we have used it to show that ivermectin inhibits the gustatory ability of vector-borne parasitic nematodes. Insight box Nematodes are powerful model organisms for understanding the sensory biology of multicellular eukaryotes, and many parasitic species cause disease in humans. Simple sensory assays performed on agarose plates have been the bedrock for establishing the neuronal, genetic, and developmental foundations for many sensory modalities in nematodes. However, these classical assays are poorly suited for translational movement of many parasitic nematodes and the sensation of water-soluble molecules (gustation). We have designed a device for high-throughput nematode sensory assays in a gel matrix. This 'gustatory microplate' is amenable to several species and reveals novel responses by free-living and parasitic nematodes to cues and drugs.

The lymphatic system plays an active role during infection, however the role of lymphatic-neutrophil interactions in host-defense responses is not well understood. During infection with pathogens such as Pseudomonas aeruginosa, Staphylococcus aureus and Yersinia pestis, neutrophils traffic from sites of infection through the lymphatic vasculature, to draining lymph nodes to interact with resident lymphocytes. This process is poorly understood, in part, due to the lack of in vitro models of the lymphatic system. Here we use a 3D microscale lymphatic vessel model to examine neutrophil-lymphatic cell interactions during host defense responses to pathogens. In previous work, we have shown that follistatin is secreted at high concentrations by lymphatic endothelial cells during inflammation. Follistatin inhibits activin A, a member of the TGF-β superfamily, and, together, these molecules form a signaling pathway that plays a role in regulating both innate and adaptive immune responses. Although follistatin and activin A are constitutively produced in the pituitary, gonads and skin, their major source in the serum and their effects on neutrophils are poorly understood. Here we report a microfluidic model that includes both blood and lymphatic endothelial vessels, and neutrophils to investigate neutrophil-lymphatic trafficking during infection with P. aeruginosa. We found that lymphatic endothelial cells produce secreted factors that increase neutrophil migration toward P. aeruginosa, and are a significant source of both follistatin and activin A during Pseudomonas infection. We determined that follistatin produced by lymphatic endothelial cells inhibits activin A, resulting in increased neutrophil migration. These data suggest that the follistatin:activin A ratio influences neutrophil trafficking during infection with higher ratios increasing neutrophil migration.

This paper describes the manufacture of geometrically inverted mammary organoids encapsulating primary mammary preadipocytes and adipocytes. Material manipulation in an array of 192 hanging drops induces cells to self-assemble into inside-out organoids where an adipose tissue core is enveloped by a cell-produced basement membrane, indicated by laminin V staining and then a continuous layer of mammary epithelial cells. This inverted tissue structure enables investigation of multiple mammary cancer subtypes, with a significantly higher extent of invasion by triple-negative MDA-MB-231 breast cancer cells compared to MCF7 cells. By seeding cancer cells into co-culture around pre-formed organoids with encapsulated preadipocytes/adipocytes, invasion through the epithelium, then into the adipose core is observable through acquisition of confocal image stacks of whole mount specimens. Furthermore, in regions of the connective tissue core where invasion occurs, there is an accumulation of collagen in the microenvironment. Suggesting that this collagen may be conducive to increased invasiveness, the anti-fibrotic drug pirfenidone shows efficacy in this model by slowing invasion. Comparison of adipose tissue derived from three different donors shows method consistency as well as the potential to evaluate donor cell-based biological variability. Insight box Geometrically inverted mammary organoids encapsulating primary preadipocytes/adipocytes (P/As) are bioengineered using a minimal amount of Matrigel scaffolding. Use of this eversion-free method is key to production of adipose mammary organoids (AMOs) where not only the epithelial polarity but also the entire self-organizing arrangement, including adipose position, is inside-out. While an epithelial-only structure can analyze cancer cell invasion, P/As are required for invasion-associated collagen deposition and efficacy of pirfenidone to counteract collagen deposition and associated invasion. The methods described strike a balance between repeatability and preservation of biological variability: AMOs form consistently across multiple adipose cell donors while revealing cancer cell invasion differences.

Breast cancer ranks as one of the most prevalent forms of cancer and stands as the primary global cause of mortality among women. Overexpression of EGFR and ER receptors or their genomic alterations leads to malignant transformation, disease aggression and is linked to poor patient survival outcomes. The clinical breast cancer patient's genomic expression, survival analysis, and computational drug-targeting approaches were used to identify best-hit phytochemicals for therapeutic purposes. Breast cancer patients have genomic alterations in EGFR (4%, n = 5699) and ER (9%, n = 8461), with the highest proportion being missense mutations. No statistically significant difference was observed in the patient survival rates between the altered and unaltered ER groups, unlike EGFR, with the lowest survival rates in the altered group. Computational screening of natural compound libraries (7711) against each EGFR (3POZ) and ER (3ERT) receptor shortlists the best-hit 3 compounds with minimum docking score (ΔG = -7.9 to -10.8), MMGBSA (-40.16 to -51.91 kcal/mol), strong intermolecular H-bonding, drug-like properties with least kd, and ki. MD simulation studies display stable RMSD, RMSF, and good residual correlation of best-hit common compounds (PubChem ID: 5281672 and 5280863) targeting both EGFR and ER receptors. In vitro, studies revealed that these common drugs exhibited a high anti-proliferative effect on MCF-7 and MDA-MB-231 breast cancer cells, with effective IC50 values (15-40 μM) and lower free energy, kd, and ki (5281672 > 5280863 > 5330286) much affecting HEK-293 non-cancerous cells, indicating the safety profile. The experimental and computational correlation studies suggest that the highly expressed EGFR and ER receptors in breast cancer patients having poor survival rates can be effectively targeted with best-hit common potent drugs with a multi-target therapeutic approach. Insight Box: The findings of this study provide valuable insights into the genomic/proteomic data, breast cancer patient's survival analysis, and EGFR and ER receptor variants structural analysis. The genetic alterations analysis of EGFR and ER/ESR1 in breast cancer patients reveals the high frequency of mutation types, which affect patient's survival rate and targeted therapies. The common best-hit compounds affect the cell survival patterns with effective IC50, drug-like properties having lower equilibrium and dissociation constants demonstrating the anti-proliferative effects. This work integrates altered receptor structural analysis, molecular interaction-based simulations, and ADMET properties to illuminate the identified best hits phytochemicals potential efficacy targeting both EGFR and ER receptors, demonstrating a multi-target therapeutic approach.

In recent years, three-dimensional (3D) cell culture has been attracting attention as a cell culture model that mimics an environment closer to that of a living organism. It is known that there is a close relationship between cell nuclear shape and cellular function, which highlights the importance of cell nucleus shape analysis in the 3D culture. On the other hand, it is difficult to observe the cell nuclei inside the 3D culture models because the penetration depth of the laser light under a microscope is limited. In this study, we adopted an aqueous iodixanol solution to the 3D osteocytic spheroids derived from mouse osteoblast precursor cells to make the spheroids transparent for 3D quantitative analysis. With a custom-made image analysis pipeline in Python, we found that the aspect ratio of the cell nuclei near the surface of the spheroid was significantly greater than that at the center, suggesting that the nuclei on the surface were deformed more than those at the center. The results also quantitatively showed that the orientation of nuclei in the center of the spheroid was randomly distributed, whereas those on the surface of the spheroid were oriented parallel to the surface of the spheroid. Our 3D quantitative method with an optical clearing technique will contribute to the 3D culture models including various organoid models to elucidate the nuclear deformation during the development of the organs. Insight box Although 3D cell culture has been a powerful tool in the fields of fundamental biology and tissue engineering, it raises the demand for quantification techniques for cell nuclear morphology in the 3D culture model. In this study, we attempted to optically clear a 3D osteocytic spheroid model using iodixanol solution for the nuclear observation inside the spheroid. Moreover, using a custom-made image analysis pipeline in Python, we successfully quantified the nuclear morphology regarding aspect ratio and orientation. Our quantitative method with the optical clearing technique will contribute to the 3D culture models such as various organoid models to elucidate the nuclear deformation during the development of the organs.

Radiation therapy, one of the most effective therapies to treat cancer, is highly toxic to healthy tissue. The delivery of radiation at ultra-high dose rates, FLASH radiation therapy (FLASH), has been shown to maintain therapeutic anti-tumor efficacy while sparing normal tissues compared to conventional dose rate irradiation (CONV). Though promising, these studies have been limited mainly to murine models. Here, we leveraged enteroids, three-dimensional cell clusters that mimic the intestine, to study human-specific tissue response to radiation. We observed enteroids have a greater colony growth potential following FLASH compared with CONV. In addition, the enteroids that reformed following FLASH more frequently exhibited proper intestinal polarity. While we did not observe differences in enteroid damage across groups, we did see distinct transcriptomic changes. Specifically, the FLASH enteroids upregulated the expression of genes associated with the WNT-family, cell-cell adhesion, and hypoxia response. These studies validate human enteroids as a model to investigate FLASH and provide further evidence supporting clinical study of this therapy. Insight Box Promising work has been done to demonstrate the potential of ultra-high dose rate radiation (FLASH) to ablate cancerous tissue, while preserving healthy tissue. While encouraging, these findings have been primarily observed using pre-clinical murine and traditional two-dimensional cell culture. This study validates the use of human enteroids as a tool to investigate human-specific tissue response to FLASH. Specifically, the work described demonstrates the ability of enteroids to recapitulate previous in vivo findings, while also providing a lens through which to probe cellular and molecular-level responses to FLASH. The human enteroids described herein offer a powerful model that can be used to probe the underlying mechanisms of FLASH in future studies.

Neurodegenerative disorders (NDDs) are known to exhibit genetic overlap and shared pathophysiology. This study aims to find the shared genetic architecture of Alzheimer's disease (AD) and Parkinson's disease (PD), two major age-related progressive neurodegenerative disorders. The gene expression profiles of GSE67333 (containing samples from AD patients) and GSE114517 (containing samples from PD patients) were retrieved from the Gene Expression Omnibus (GEO) functional genomics database managed by the National Center for Biotechnology Information. The web application GREIN (GEO RNA-seq Experiments Interactive Navigator) was used to identify differentially expressed genes (DEGs). A total of 617 DEGs (239 upregulated and 379 downregulated) were identified from the GSE67333 dataset. Likewise, 723 DEGs (378 upregulated and 344 downregulated) were identified from the GSE114517 dataset. The protein-protein interaction networks of the DEGs were constructed, and the top 50 hub genes were identified from the network of the respective dataset. Of the four common hub genes between two datasets, C-X-C chemokine receptor type 4 (CXCR4) was selected due to its gene expression signature profile and the same direction of differential expression between the two datasets. Mavorixafor was chosen as the reference drug due to its known inhibitory activity against CXCR4 and its ability to cross the blood-brain barrier. Molecular docking and molecular dynamics simulation of 51 molecules having structural similarity with Mavorixafor was performed to find two novel molecules, ZINC49067615 and ZINC103242147. This preliminary study might help predict molecular targets and diagnostic markers for treating Alzheimer's and Parkinson's diseases. Insight Box Our research substantiates the therapeutic relevance of CXCR4 inhibitors for the treatment of Alzheimer's and Parkinson's diseases. We would like to disclose the following insights about this study. We found common signatures between Alzheimer's and Parkinson's diseases at transcriptional levels by analyzing mRNA sequencing data. These signatures were used to identify putative therapeutic agents for these diseases through computational analysis. Thus, we proposed two novel compounds, ZINC49067615 and ZINC103242147, that were stable, showed a strong affinity with CXCR4, and exhibited good pharmacokinetic properties. The interaction of these compounds with major residues of CXCR4 has also been described.