Deep Learning Powered Identification of Differentiated Early Mesoderm Cells from Pluripotent Stem Cells.

Pluripotent stem cells can be differentiated into all three germ-layers including ecto-, endo-, and mesoderm in vitro. However, the early identification and rapid characterization of each germ-layer in response to chemical and physical induction of differentiation is limited. This is a long-standing issue for rapid and high-throughput screening to determine lineage specification efficiency. Here, we present deep learning (DL) methodologies for predicting and classifying early mesoderm cells differentiated from embryoid bodies (EBs) based on cellular and nuclear morphologies. Using a transgenic murine embryonic stem cell (mESC) line, namely OGTR1, we validated the upregulation of mesodermal genes (Brachyury (T): DsRed) in cells derived from EBs for the deep learning model training. Cells were classified into mesodermal and non-mesodermal (representing endo- and ectoderm) classes using a convolutional neural network (CNN) model called InceptionV3 which achieved a very high classification accuracy of 97% for phase images and 90% for nuclei images. In addition, we also performed image segmentation using an Attention U-Net CNN and obtained a mean intersection over union of 61% and 69% for phase-contrast and nuclear images, respectively. This work highlights the potential of integrating cell culture, imaging technologies, and deep learning methodologies in identifying lineage specification, thus contributing to the advancements in regenerative medicine. Collectively, our trained deep learning models can predict the mesoderm cells with high accuracy based on cellular and nuclear morphologies.

Fitness Costs of Antibiotic Resistance Impede the Evolution of Resistance to Other Antibiotics.

Antibiotic resistance is a major threat to global health, claiming the lives of millions every year. With a nearly dry antibiotic development pipeline, novel strategies are urgently needed to combat resistant pathogens. One emerging strategy is the use of sequential antibiotic therapy, postulated to reduce the rate at which antibiotic resistance evolves. Here, we use the soft agar gradient evolution (SAGE) system to carry out high-throughput in vitro bacterial evolution against antibiotic pressure. We find that evolution of resistance to the antibiotic chloramphenicol (CHL) severely affects bacterial fitness, slowing the rate at which resistance to the antibiotics nitrofurantoin and streptomycin emerges. In vitro acquisition of compensatory mutations in the CHL-resistant cells markedly improves fitness and nitrofurantoin adaptation rates but fails to restore rates to wild-type levels against streptomycin. Genome sequencing reveals distinct evolutionary paths to resistance in fitness-impaired populations, suggesting resistance trade-offs in favor of mitigation of fitness costs. We show that the speed of bacterial fronts in SAGE plates is a reliable indicator of adaptation rates and evolutionary trajectories to resistance. Identification of antibiotics whose mutational resistance mechanisms confer stable impairments may help clinicians prescribe sequential antibiotic therapies that are less prone to resistance evolution.

Hybrid AI models allow label-free identification and classification of pancreatic tumor repopulating cell population.

Human pancreatic cancer cell lines harbor a small population of tumor repopulating cells (TRCs). Soft 3D fibrin gel allows efficient selection and growth of these tumorigenic TRCs. However, rapid and high-throughput identification and classification of pancreatic TRCs remain technically challenging. Here, we developed deep learning (DL) models paired with machine learning (ML) models to readily identify and classify 3D fibrin gel-selected TRCs into sub-types. Using four different human pancreatic cell lines, namely, MIA PaCa-2, PANC-1, CFPAC-1, and HPAF-II, we classified 3 main sub-types to be present within the TRC population. Our best model was an Inception-v3 convolutional neural network (CNN) used as a feature extractor paired with a Support Vector Machine (SVM) classifier with radial basis function (rbf) kernel which obtained a test accuracy of 90%. In addition, we compared this hybrid method of supervised classification with other methods of supervised classifications and showed that our working model outperforms others. With the help of unsupervised machine learning algorithms, we also validated that the pancreatic TRC subpopulation can be clustered into 3 sub-types. Collectively, our robust model can detect and readily classify tumorigenic TRC subpopulation label-free in a high-throughput fashion which can be very beneficial in clinical settings.

Reduced Cell-ECM Interactions in the EpiSC Colony Center Cause Heterogeneous Differentiation.

Mechanoregulation of cell-extracellular matrix (ECM) interactions are crucial for dictating pluripotent stem cell differentiation. However, not all pluripotent cells respond homogeneously which results in heterogeneous cell populations. When cells, such as mouse epiblast stem cells (EpiSCs), are cultured in clusters, the heterogeneity effect during differentiation is even more pronounced. While past studies implicated variations in signaling pathways to be the root cause of heterogeneity, the biophysical aspects of differentiation have not been thoroughly considered. Here, we demonstrate that the heterogeneity of EpiSC differentiation arises from differences in the colony size and varying degrees of interactions between cells within the colonies and the ECM. Confocal imaging demonstrates that cells in the colony periphery established good contact with the surface while the cells in the colony center were separated by an average of 1-2 µm from the surface. Traction force measurements of the cells within the EpiSC colonies show that peripheral cells generate large tractions while the colony center cells do not. A finite element modeling of EpiSC colonies shows that tractions generated by the cells at the colony periphery lift off the colony center preventing the colony center from undergoing differentiation. Together, our results demonstrate a biophysical regulation of heterogeneous EpiSC colony differentiation.

Driven Radical Motion Enhances Cryptochrome Magnetoreception: Toward Live Quantum Sensing.

The mechanism underlying magnetoreception has long eluded explanation. A popular hypothesis attributes this sense to the quantum coherent spin dynamics and spin-selective recombination reactions of radical pairs in the protein cryptochrome. However, concerns about the validity of the hypothesis have been raised because unavoidable inter-radical interactions, such as the strong electron-electron dipolar coupling, appear to suppress its sensitivity. We demonstrate that sensitivity can be restored by driving the spin system through a modulation of the inter-radical distance. It is shown that this dynamical process markedly enhances geomagnetic field sensitivity in strongly coupled radical pairs via Landau-Zener-Stückelberg-Majorana transitions between singlet and triplet states. These findings suggest that a "live" harmonically driven magnetoreceptor can be more sensitive than its "dead" static counterpart.

Diisonitrile Lipopeptides Mediate Resistance to Copper Starvation in Pathogenic Mycobacteria.

Bacterial pathogens and their hosts engage in intense competition for critical nutrients during infection, including metals such as iron, copper, and zinc. Some metals are limited by the host, and some are deployed by the host as antimicrobials. To counter metal limitation, pathogens deploy high-affinity metal acquisition systems, best exemplified by siderophores to acquire iron. Although pathogen strategies to resist the toxic effects of high Cu have been elucidated, the role of Cu starvation and the existence of Cu acquisition systems are less well characterized. In this study, we examined the role of diisonitrile chalkophores of pathogenic mycobacteria, synthesized by the enzymes encoded by the virulence-associated nrp gene cluster, in metal acquisition. nrp gene cluster expression is strongly induced by starvation or chelation of Cu but not starvation of Zn or excess Cu. Mycobacterium tuberculosis and Mycobacterium marinum strains lacking the nrp-encoded nonribosomal peptide sythetase, the fadD10 adenylate-forming enzyme, or the uncharacterized upstream gene ppe1 are all sensitized to Cu, but not Zn, starvation. This low Cu sensitivity is rescued by genetic complementation or by provision of a synthetic diisonitrile chalkophore. These data demonstrate that diisonitrile lipopeptides in mycobacteria are chalkophores that facilitate survival under Cu-limiting conditions and suggest that Cu starvation is a relevant stress for M. tuberculosis in the host. IMPORTANCE Bacterial pathogens and their hosts engage in intense competition for nutrients, including metals. Mycobacterium tuberculosis, the cause of tuberculosis, lives within host macrophages and is subject to diverse stresses, including metal excess and metal limitation. In this study, we demonstrated that the nrp gene cluster, required for M. tuberculosis virulence and which directs synthesis of diisonitrile lipopeptides, mediates copper acquisition. Copper, but not zinc, deprivation strongly induces diisonitrile biosynthesis, and M. tuberculosis strains lacking the nrp gene, or the associated genes fadD10 or ppe1, are all sensitized to copper chelation or copper deprivation. These results establish a copper binding, or chalkophore, system in M. tuberculosis and indicate that resistance to copper restriction plays an important role in the ability of this global pathogen to cause infection.

Additive Manufacturing of Viscoelastic Polyacrylamide Substrates for Mechanosensing Studies.

Polymerized polyacrylamide (PAA) substrates are linearly elastic hydrogels that are widely used in mechanosensing studies due to their biocompatibility, wide range of functionalization capability, and tunable mechanical properties. However, such cellular response on purely elastic substrates, which do not mimic the viscoelastic living tissues, may not be physiologically relevant. Because the cellular response on 2D viscoelastic PAA substrates remains largely unknown, we used stereolithography (SLA)-based additive manufacturing technique to create viscoelastic PAA substrates with tunable mechanical properties that allow us to identify physiologically relevant cellular behaviors. Three PAA substrates of different complex moduli were fabricated by SLA. By embedding fluorescent markers during the additive manufacturing of the substrates, we show a homogeneous and uniform composition throughout, which conventional manufacturing techniques cannot produce. Rheological investigation of the additively manufactured PAA substrates shows a viscoelastic behavior with a 5-10% loss moduli compared to their elastic moduli, mimicking the living tissues. To understand the cell mechanosensing on the dissipative PAA substrates, single live cells were seeded on PAA substrates to establish the basic relationships between cell traction, cytoskeletal prestress, and cell spreading. With the increasing substrate moduli, we observed a concomitant increase in cellular traction and prestress, but not cell spreading, suggesting that cell spreading can be decoupled from traction and intracellular prestress in physiologically relevant environments. Together, additively manufactured PAA substrates fill the void of lacking real tissue like viscoelastic materials that can be used in a variety of mechanosensing studies with superior reproducibility.

Forces in stem cells and cancer stem cells.

Endogenous and exogenous forces are critical in physiology and pathology of the human body. Increasing evidence suggests that these forces, mechanics, and force-associated signaling are essential in regulating functions of living cells. Here we review advances in understanding the impact of forces and mechanics on functions and fate of embryonic stem cells, adult stem cells, and cancer stem cells and the pathways of mechanotransduction in cells. Stem-cells based models are useful in understanding how forces influence physiology, pathology, and embryonic development, which is incompletely understood, especially for mammals. We highlight increasing efforts and emerging favorable clinical outcomes in mechanomedicine, application of mechanobiology to medicine. Major progresses in mechanobiology, the pillar of mechanomedicine and mechanohealth (application of mechanobiology to health), are pivotal in understanding the life of force and making substantial advances in medicine and health.

Single-Cell RNA Sequencing with Spatial Transcriptomics of Cancer Tissues.

Single-cell RNA sequencing (RNA-seq) techniques can perform analysis of transcriptome at the single-cell level and possess an unprecedented potential for exploring signatures involved in tumor development and progression. These techniques can perform sequence analysis of transcripts with a better resolution that could increase understanding of the cellular diversity found in the tumor microenvironment and how the cells interact with each other in complex heterogeneous cancerous tissues. Identifying the changes occurring in the genome and transcriptome in the spatial context is considered to increase knowledge of molecular factors fueling cancers. It may help develop better monitoring strategies and innovative approaches for cancer treatment. Recently, there has been a growing trend in the integration of RNA-seq techniques with contemporary omics technologies to study the tumor microenvironment. There has been a realization that this area of research has a huge scope of application in translational research. This review article presents an overview of various types of single-cell RNA-seq techniques used currently for analysis of cancer tissues, their pros and cons in bulk profiling of transcriptome, and recent advances in the techniques in exploring heterogeneity of various types of cancer tissues. Furthermore, we have highlighted the integration of single-cell RNA-seq techniques with other omics technologies for analysis of transcriptome in their spatial context, which is considered to revolutionize the understanding of tumor microenvironment.

Klebsiella pneumoniae pathogenicity in silk moth larvae infection model.

The emergence of antibiotic resistant bacteria is a major health concern worldwide in recent years. The objective of this study is to establish the larvae of the silk moth (commonly known as silkworm), Bombyx mori as an infection model to study antibacterial effect of antibiotics against Klebsiella pneumoniae. In this study, the pathogenicity of a K. pneumoniae strain isolated from food to silkworm larvae was examined. Within 72 h of bacterial injection, all silkworm larvae were killed in a dose-dependent manner with their body color turning into black due to increased melanization. Bacterial numbers in the larval hemolymph (blood) significantly increased after 9 h of infection with a decrease in viable circulatory hemocytes in hemolymph. When presented with bacteria laden leaves, larvae did not eat but injection of bacteria directly into the midgut killed larvae within 12 h with a higher load required in comparison to that required for the killing by hemolymph injection. Administration of four different antibiotics into larval hemolymph showed therapeutic effect at different doses with varying efficacies against hemolymph-injected K. pneumoniae. These results indicate that the silkworm larvae can be used as an infection model not only to study the pathogenicity of K. pneumoniae but also to perform rapid screening for the identification of antibiotics effective against multidrug-resistant strains of K. pneumoniae.

Cytoskeletal prestress: The cellular hallmark in mechanobiology and mechanomedicine.

Increasing evidence demonstrates that mechanical forces, in addition to soluble molecules, impact cell and tissue functions in physiology and diseases. How living cells integrate mechanical signals to perform appropriate biological functions is an area of intense investigation. Here, we review the evidence of the central role of cytoskeletal prestress in mechanotransduction and mechanobiology. Elevating cytoskeletal prestress increases cell stiffness and reinforces cell stiffening, facilitates long-range cytoplasmic mechanotransduction via integrins, enables direct chromatin stretching and rapid gene expression, spurs embryonic development and stem cell differentiation, and boosts immune cell activation and killing of tumor cells whereas lowering cytoskeletal prestress maintains embryonic stem cell pluripotency, promotes tumorigenesis and metastasis of stem cell-like malignant tumor-repopulating cells, and elevates drug delivery efficiency of soft-tumor-cell-derived microparticles. The overwhelming evidence suggests that the cytoskeletal prestress is the governing principle and the cellular hallmark in mechanobiology. The application of mechanobiology to medicine (mechanomedicine) is rapidly emerging and may help advance human health and improve diagnostics, treatment, and therapeutics of diseases.

3D bioprinting of engineered breast cancer constructs for personalized and targeted cancer therapy.

The bioprinting technique with specialized tissue production allows the study of biological, physiological, and behavioral changes of cancerous and non-cancerous tissues in response to pharmacological compounds in personalized medicine. To this end, to evaluate the efficacy of anticancer drugs before entering the clinical setting, tissue engineered 3D scaffolds containing breast cancer and derived from the especially patient, similar to the original tissue architecture, can potentially be used. Despite recent advances in the manufacturing of 3D bioprinted breast cancer tissue (BCT), many studies still suffer from reproducibility primarily because of the uncertainty of the materials used in the scaffolds and lack of printing methods. In this review, we present an overview of the breast cancer environment to optimize personalized treatment by examining and identifying the physiological and biological factors that mimic BCT. We also surveyed the materials and techniques related to 3D bioprinting, i.e, 3D bioprinting systems, current strategies for fabrication of 3D bioprinting tissues, cell adhesion and migration in 3D bioprinted BCT, and 3D bioprinted breast cancer metastasis models. Finally, we emphasized on the prospective future applications of 3D bioprinted cancer models for rapid and accurate drug screening in breast cancer.

COL2A1 Is a Novel Biomarker of Melanoma Tumor Repopulating Cells.

Soft 3D-fibrin-gel selected tumor repopulating cells (TRCs) from the B16F1 melanoma cell line exhibit extraordinary self-renewal and tumor-regeneration capabilities. However, their biomarkers and gene regulatory features remain largely unknown. Here, we utilized the next-generation sequencing-based RNA sequencing (RNA-seq) technique to discover novel biomarkers and active gene regulatory features of TRCs. Systems biology analysis of RNA-seq data identified differentially expressed gene clusters, including the cell adhesion cluster, which subsequently identified highly specific and novel biomarkers, such as Col2a1, Ncam1, F11r, and Negr1. We validated the expression of these genes by real-time qPCR. The expression level of Col2a1 was found to be relatively low in TRCs but twenty-fold higher compared to the parental control cell line, thus making the biomarker very specific for TRCs. We validated the COL2A1 protein by immunofluorescence microscopy, showing a higher expression of COL2A1 in TRCs compared to parental control cells. KEGG pathway analysis showed the JAK/STAT, hypoxia, and Akt signaling pathways to be active in TRCs. Besides, the aerobic glycolysis pathway was found to be very active, indicating a typical Warburg Effect on highly tumorigenic cells. Together, our study revealed highly specific biomarkers and active cell signaling pathways of melanoma TRCs that can potentially target and neutralize TRCs.

Machine Learning Approach to Raman Spectrum Analysis of MIA PaCa-2 Pancreatic Cancer Tumor Repopulating Cells for Classification and Feature Analysis.

A machine learning approach is applied to Raman spectra of cells from the MIA PaCa-2 human pancreatic cancer cell line to distinguish between tumor repopulating cells (TRCs) and parental control cells, and to aid in the identification of molecular signatures. Fifty-one Raman spectra from the two types of cells are analyzed to determine the best combination of data type, dimension size, and classification technique to differentiate the cell types. An accuracy of 0.98 is obtained from support vector machine (SVM) and k-nearest neighbor (kNN) classifiers with various dimension reduction and feature selection tools. We also identify some possible biomolecules that cause the spectral peaks that led to the best results.

A quartz crystal microbalance based study reveals living cell loading rate via αvß3 integrins.

Cellular interactions with the microenvironment are mediated by ligand-receptor bonds. Such ligand-receptor bond dynamics is known to be heavily dependent on the loading rate. However, the physiologically-relevant loading rate of living cells remains unknown. Here, using a quartz crystal microbalance (QCM), we developed a bulk-force sensing platform to semi-quantitatively detect the rate of cellular force application during early stages of cell adhesion and spreading. Atop an Au-coated quartz crystal, covalently linked self-assembled monolayers (SAM) were used to immobilize cyclic-RGDfK peptides that can interact with the αvß3 integrins on cells. The QCM detects the changes in resonant frequency of the vibrating crystal due to the cellular activity/probing (force application) on the QCM surface. The corresponding changes in mass on the surface, proportional to the rate of force application, arise from the cellular interactions with the functionalized surface were calculated. The loading rate of living cells was found to be ∼80-115 pN/s. Collectively, our results revealed a fundamental feature of cell adhesion and spreading providing valuable information regarding cellular interactions with the extracellular matrix.

Cdc42-dependent modulation of rigidity sensing and cell spreading in tumor repopulating cells.

Recently, a robust mechanical method has been established to isolate a small subpopulation of highly tumorigenic tumor repopulating cells (TRCs) from parental melanoma cells. In order to characterize the molecular and mechanical properties of TRCs, we utilized the tension gauge tether (TGT) single-molecule platform and investigated force requirements during early cell spreading events. TRCs required the peak single molecular tension of around 40 pN through integrins for initial adhesion like the parental control cells, but unlike the control cells, they did not spread and formed very few mature focal adhesions (FAs). Single molecule resolution RNA quantification of three Rho GTPases showed that downregulation of Cdc42, but not Rac1, is responsible for the unusual biophysical features of TRCs and that a threshold level of Cdc42 transcripts per unit cell area is required to initiate cell spreading. Cdc42 overexpression rescued TRC spreading through FA formation and restored the sensitivity to tension cues such that TRCs, like parental control cells, increase cell spreading with increasing single-molecular tension cues. Our single molecule studies identified an unusual biophysical feature of suppressed spreading of TRCs that may enable us to distinguish TRC population from a pool of heterogeneous tumor cell population.

Rerouting mesenchymal stem cell trajectory towards epithelial lineage by engineering cellular niche.

Mesenchymal stromal/stem cells (MSCs) are multipotent cells that offer a promising outcome in the field of regenerative medicine. MSCs are present in various tissues including bone marrow, fat, skin, and placenta. The interest in clinical application of these mesoderm-derived MSCs is primarily fueled by their high self-renewal capacity and multipotency. Although, early studies indicated limited differentiation capacity of MSCs into same cell lineages from which they were isolated, subsequent investigations showed differentiation potential into other cell types of mesoderm origin including osteoblasts, adipocytes, fibroblasts, cardiomyocytes, and chondrocytes. Furthermore, MSCs exhibit a remarkable feature of transdifferentiation into ectodermal, neuroectodermal, and endodermal cells, phenomena referred to as 'stem cell plasticity'. This opened the possibility of clinical applications of MSCs in the regeneration of other tissues like corneal reconstruction, treatment of acute lung injury, oral mucosal regeneration, homing of MSCs for regeneration at sites of injury etc. Though several evidence have accrued demonstrating this phenomenon, there is still a gap in understanding the molecular mechanism of such transitions which will be important to efficiently control the process. Interestingly, the process can be drawn a parallel with the Mesenchymal to Epithelial Transitions (MET) that takes place inside the body during embryonic development or certain pathophysiological conditions. In this review, a brief attempt is first made to understand the evidence of MSC transdifferentiation based on the current knowledge about MET. We then specifically focus on systematic presentation and analysis of the microenvironment factors involved in MSC transdifferentiation to epithelial lineages which would have applications in regenerative medicine.

Biophysical Tools to Study Cellular Mechanotransduction.

The cell membrane is the interface that volumetrically isolates cellular components from the cell's environment. Proteins embedded within and on the membrane have varied biological functions: reception of external biochemical signals, as membrane channels, amplification and regulation of chemical signals through secondary messenger molecules, controlled exocytosis, endocytosis, phagocytosis, organized recruitment and sequestration of cytosolic complex proteins, cell division processes, organization of the cytoskeleton and more. The membrane's bioelectrical role is enabled by the physiologically controlled release and accumulation of electrochemical potential modulating molecules across the membrane through specialized ion channels (e.g., Na⁺, Ca2+, K⁺ channels). The membrane's biomechanical functions include sensing external forces and/or the rigidity of the external environment through force transmission, specific conformational changes and/or signaling through mechanoreceptors (e.g., platelet endothelial cell adhesion molecule (PECAM), vascular endothelial (VE)-cadherin, epithelial (E)-cadherin, integrin) embedded in the membrane. Certain mechanical stimulations through specific receptor complexes induce electrical and/or chemical impulses in cells and propagate across cells and tissues. These biomechanical sensory and biochemical responses have profound implications in normal physiology and disease. Here, we discuss the tools that facilitate the understanding of mechanosensitive adhesion receptors. This article is structured to provide a broad biochemical and mechanobiology background to introduce a freshman mechano-biologist to the field of mechanotransduction, with deeper study enabled by many of the references cited herein.

Defining Single Molecular Forces Required for Notch Activation Using Nano Yoyo.

Notch signaling, involved in development and tissue homeostasis, is activated at the cell-cell interface through ligand-receptor interactions. Previous studies have implicated mechanical forces in the activation of Notch receptor upon binding to its ligand. Here we aimed to determine the single molecular force required for Notch activation by developing a novel low tension gauge tether (LTGT). LTGT utilizes the low unbinding force between single-stranded DNA (ssDNA) and Escherichia coli ssDNA binding protein (SSB) (∼4 pN dissociation force at 500 nm/s pulling rate). The ssDNA wraps around SSB and, upon application of force, unspools from SSB, much like the unspooling of a yoyo. One end of this nano yoyo is attached to the surface though SSB, while the other end presents a ligand. A Notch receptor, upon binding to its ligand, is believed to undergo force-induced conformational changes required for activating downstream signaling. If the required force for such activation is larger than 4 pN, ssDNA will unspool from SSB, and downstream signaling will not be activated. Using these LTGTs, in combination with the previously reported TGTs that rupture double-stranded DNA at defined forces, we demonstrate that Notch activation requires forces between 4 and 12 pN, assuming an in vivo loading rate of 60 pN/s. Taken together, our study provides a direct link between single-molecular forces and Notch activation.

Constructing modular and universal single molecule tension sensor using protein G to study mechano-sensitive receptors.

Recently a variety of molecular force sensors have been developed to study cellular forces acting through single mechano-sensitive receptors. A common strategy adopted is to attach ligand molecules on a surface through engineered molecular tethers which report cell-exerted tension on receptor-ligand bonds. This approach generally requires chemical conjugation of the ligand to the force reporting tether which can be time-consuming and labor-intensive. Moreover, ligand-tether conjugation can severely reduce the activity of protein ligands. To address this problem, we developed a Protein G (ProG)-based force sensor in which force-reporting tethers are conjugated to ProG instead of ligands. A recombinant ligand fused with IgG-Fc is conveniently assembled with the force sensor through ProG:Fc binding, therefore avoiding ligand conjugation and purification processes. Using this approach, we determined that molecular tension on E-cadherin is lower than dsDNA unzipping force (nominal value: 12 pN) during initial cadherin-mediated cell adhesion, followed by an escalation to forces higher than 43 pN (nominal value). This approach is highly modular and potentially universal as we demonstrate using two additional receptor-ligand interactions, P-selectin &PSGL-1 and Notch &DLL1.