Specialized protrusions coordinate migratory border cell cluster cohesion via Scribble, Cdep, and Rac

Apicobasal polarity is a defining characteristic of epithelial cells and its disruption is a cancer hallmark. Distinct apical and basolateral protein modules antagonize each other to establish separate membrane domains. These modules interact with dozens of potential effector proteins. Here we describe polarity protein localization and function within a migrating epithelial cell cluster and identify a functionally significant effector protein. In Drosophila egg chambers, border cells delaminate from the follicular epithelium and migrate collectively. We report that the basolateral protein Scribble is required for border cell cluster cohesion and migration. The basolateral module localizes the Rac guanine nucleotide exchange factor Cdep to membranes, and Cdep knockdown phenocopies Scribble cluster cohesion defects. Remarkably, membrane targeting of Cdep is sufficient to partially suppress multiple Scribble phenotypes. We describe specialized basolateral protrusions that promote cluster cohesion. Scribble restricts these protrusions from encroaching onto the apical domain. Thus, a major function of the basolateral module is to localize Cdep, promoting specialized protrusions, cluster cohesion, and collective migration.

Bionanotechnology Approaches to Combat Biofilms and Drug Resistance

This chapter deals with the formation of biofilms, their resistance to antibacterial agents, the importance and risk of biofilms, and nanotechnology methods for biofilm control in the food industry. Biofilm is a multi-layer cell cluster embedded in an organic polymer matrix, which protects microbial cells from environmental stress, antibiotics, and disinfectants. Microorganisms that live in contact points and the environment in food processing are mostly harmful because the microbial community in the wrong location can lead to contamination of the surfaces and products produced during the processing. When new nanomaterials (for example, silver or copper are incorporated) are used, the growth of surface biofilms can also be reduced. In recent years, new nanotechnology-based antimicrobials have been designed to kill planktonic, antibiotic-resistant bacteria, but additional requirements rather than the mere killing of suspended bacteria must be met to combat biofilm-infections.

Characterization of T-Cell Exhaustion in Rapid Progressing Multiple Myeloma Using Cross Center Scrna-Seq Study

Introduction: Multiple myeloma (MM) is a complex hematological malignancy with the heterogenous immune bone marrow (BM) environment contributing to tumor growth, drug resistance, and immune escape. T-Cells play a critical role in the clearance of malignant plasma cells from the tumor environment. However, T-Cells in multiple myeloma demonstrate impaired cytotoxicity, proliferation, and cytokine production due to the activation of immune inhibitory receptors from ligands produced by the myeloma cells. In this study, we investigate the behavior of T-Cells in MM patients by using single-cell RNA-Seq (scRNA-Seq) to compare the transcriptomic profiles of BM T-Cells of patients with rapid progressing (FP; PFS < 18mo) and non-progressing (NP; PFS > 4yrs) disease. Methods: Newly diagnosed MM patients (n=18) from the Multiple Myeloma Research Foundation (MMRF) CoMMpass study (NCT01454297) were identified as either rapid progressors or non-progressors based on their progression free survival since diagnosis. To capture transcriptomic data, scRNA-Seq was performed on 48 aliquots of frozen CD138-negative BM cells at three medical centers/universities (Beth Israel Deaconess Medical Center, Boston, Washington University in St. Louis, and Mount Sinai School of Medicine, NYC). Samples were collected at diagnosis prior to treatment. Surface marker expression for 29 proteins was captured for at least one sample per patient using CITE-Seq. After integration and batch correction, clustering was performed to identify cells of T or NK lineage. Uniform Manifold Approximation and Projection (UMAP) and differential expression were used to identify T-Lymphoid subtypes, and differences in NP and FP samples. Results: In this study, single cell transcriptomic profiles were identified for ~102,207 cells from 48 samples of 18 MM patients. 40,328 T (CD3+) and NK (CD3-, NKG7+) cells were isolated, and subclustered for further analysis (Fig 1A). Using differentially expressed markers for each cluster, the T-Lymphoid subset was refined into seven subtypes, consisting of various CD4+ T-Cells, CD8+ T-Cells, and NK cells (Fig 1B). The CD8+ cells were divided into three distinct phenotypes, namely a GZMK-, GZMB- CD8+ T-Cell cluster, a GZMK+ CD8+ Exhausted T-Cell cluster enriched in TIGIT and multiple chemokines (CCL3, CCL4, XCL2), and a GZMB+ NkT cluster enriched in cytolytic markers (PRF1, GNLY, NKG7) (Fig 1C). Differential expression between NP and FP samples in this CD8+ subset showed enrichment of the NkT cytotoxic markers in NP samples, while FP samples were enriched in the CD8+ Exhausted chemokine markers (Fig 1D). Furthermore, the proportion of CD8+ Exhausted T-Cells was enriched in FP samples (p.val < 0.05) (Fig 1E). Exhaustion markers were measured through both RNA and surface marker levels. In RNA, TIGIT was uniquely associated with the FP-enriched CD8+ Exhausted T-Cell cluster, and CD160 was uniquely expressed in FP samples (Fig 1F). CITE-Seq surface marker expression confirms enrichment of both TIGIT and PD1 in the CD8+ Exhausted T-Cell cluster, and along with more exhaustion in FP samples (p.val < 0.01). Conclusion: In this study, we have identified significant differences in T-Cell activity in patients with non-progressing and rapid-progressing multiple myeloma. T-Cells in rapid progressing patients appear to be in a suppressed state, with low cytolytic activity and enriched exhaustion markers. This GZMK+ T-Cell population shows strong similarities with an aging-associated subtype of effector memory T-Cells found to be enriched in older populations (Mogilenko et al, Immunity 54, 2021). These findings will be further validated in an expanded study, consisting both of a larger number of samples, and multiple samples at different timepoints from the same patient. Figure 1 Figure 1. Disclosures Jayasinghe: MMRF: Consultancy; WUGEN: Consultancy. Vij: BMS: Research Funding; Takeda: Honoraria, Research Funding; Sanofi: Honoraria, Research Funding; BMS: Honoraria; GSK: Honoraria; Oncopeptides: Honoraria; Karyopharm: Honoraria; CareDx: Honoraria; Legend: Honoraria; Biegene: Honoraria; Adaptive: Honoraria; Harpoon: Honoraria. Kumar: Carsgen: Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Beigene: Consultancy; Bluebird Bio: Consultancy; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Tenebio: Research Funding; Oncopeptides: Consultancy; Antengene: Consultancy, Honoraria; Roche-Genentech: Consultancy, Research Funding; Merck: Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; Amgen: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding. Avigan: Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Research Funding; Kite Pharma: Consultancy, Research Funding; Juno: Membership on an entity's Board of Directors or advisory committees; Partner Tx: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Aviv MedTech Ltd: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Legend Biotech: Membership on an entity's Board of Directors or advisory committees; Chugai: Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy; Parexcel: Consultancy; Takeda: Consultancy; Sanofi: Consultancy.

687 MDSC gene expression analysis in patients with cancer and the response to inhibition of Bruton's tyrosine kinase

BackgroundMyeloid-derived suppressor cells (MDSC) are an immunosuppressive immature population of myeloid cells that are elevated in cancer patients. Increased levels of MDSC has been linked to dysregulated anti-tumor responses and reduced efficacy of immune checkpoint therapies thus making them an attractive target. MDSC express Bruton's tyrosine kinase (BTK) and can be depleted using ibrutinib, an FDA-approved irreversible inhibitor of BTK. BTK inhibition leads to reduced MDSC expansion/function in murine models and significantly improved activity of anti-PD-1 antibodies. In this study, single cell RNA-seq (scRNA-seq) was used to characterize the gene expression of MDSC from different cancer types and the effect of ibrutinib on MDSC gene expression.MethodsPeripheral blood mononuclear cells were isolated from patients with melanoma (n=2), head & neck (n=1), and breast cancer (n=1). MDSC were isolated via fluorescence activated cell sorting. MDSC isolated from melanoma patients (n=2) were treated in vitro for 4h with 1 uM ibrutinib or DMSO and scRNA-seq was performed using the Chromium 10x Genomics platform. ScRNA-seq samples were analyzed using the standard integrative workflow of Seurat v3, which addresses the sample heterogeneity. Cell clusters were identified using Seurat and annotated using SingleRversion3.12. Identification of gene markers for each cell cluster and cell-cluster-specific differential expression analyses were conducted using Seurat.ResultsBaseline gene expression of MDSC from patients with breast and head & neck cancer revealed similarities among the top expressed genes (S100A8, VCAN, and LYZ). In vitro ibrutinib treatment of MDSC from patients with melanoma resulted in significant changes in gene expression within the MDSC cluster compared to DMSO treatment. GBP1(-1.72 log fold change), IL 1β(-1.27 log fold change), and CXCL8(-0.63 log fold change) were among the top downregulated genes (p<0.001) and RGS2 (0.68 log fold change) and ABHD5(0.52 log fold change) were among the top upregulated genes (p<0.001). MDSC subset (PMN-MDSC, M-MDSC, early-MDSC, and CD14+/CD15+ double positive) gene expression changes mirrored total MDSC gene changes. Ingenuity pathway analysis revealed significant downregulated pathways including TREM1 (p<0.001), nitric oxide signaling (p<0.003), and IL-6 signaling (p<0.004). Multiple genes associated with cellular movement (CXCL8, CXCL10) and activation of macrophages (CXCL10, CCL3) were downregulated (p<0.001). PCR analysis on isolated melanoma MDSC (n=2) treated in vitro with ibrutinib verified downregulation of CXCL8 (0.42 fold decrease, p<0.05) and CXCL10 (0.40 fold decrease, p<0.001).ConclusionsAnalysis via scRNA-seq revealed similar gene expression patterns for MDSC from different cancer patients. There was downregulation of multiple genes and pathways important to MDSC function and migration after BTK inhibition.Ethics ApprovalThe study obtained ethics approval. IRB# 1999C0348

Detection of Metastatic Tumor Cells in the Bone Marrow Aspirate Smears by Artificial Intelligence (AI)-Based Morphogo System

IntroductionMetastatic carcinomas of bone marrow (MCBM) are characterized as tumors of non-hematopoietic origin spreading to the bone marrow through blood or lymphatic circulation. The diagnosis is critical for tumor staging, treatment selection and prognostic risk stratification. However, the identification of metastatic carcinoma cells on bone marrow aspiration smears is technically challenging by conventional microscopic screening.ObjectiveThe aim of this study is to develop an automatic recognition system using deep learning algorithms applied to bone marrow cells image analysis. The system takes advantage of an artificial intelligence (AI)-based method in recognizing metastatic atypical cancer clusters and promoting rapid diagnosis.MethodsWe retrospectively reviewed metastatic non-hematopoietic malignancies in bone marrow aspirate smears collected from 60 cases of patients admitted to Zhongshan Hospital. High resolution digital bone marrow aspirate smear images were generated and automatically analyzed by Morphogo AI based system. Morphogo system was trained and validated using 20748 cell cluster images from randomly selected 50 MCBM patients. 5469 pre-classified cell cluster images from the remaining 10 MCBM patients were used to test the recognition performance between Morphogo and experienced pathologists.ResultsMorphogo exhibited a sensitivity of 56.6%, a specificity of 91.3%, and an accuracy of 82.2% in the recognition of metastatic cancer cells. Morphogo’s classification result was in general agreement with the conventional standard in the diagnosis of metastatic cancer clusters, with a Kappa value of 0.513. The test results between Morphogo and pathologists H1, H2 and H3 agreement demonstrated a reliability coefficient of 0.827. The area under the curve (AUC) for Morphogo to diagnose the cancer cell clusters was 0.865.ConclusionIn patients with clinical history of cancer, the Morphogo system was validated as a useful screening tool in the identification of metastatic cancer cells in the bone marrow aspirate smears. It has potential clinical application in the diagnostic assessment of metastatic cancers for staging and in screening MCBM during morphology examination when the symptoms of the primary site are indolent.

Concurrence of overt Cushing’s syndrome and primary aldosteronism accompanied by aldosterone-producing cell cluster in adjacent adrenal cortex: case report

Background Various adrenal disorders including primary aldosteronism and Cushing’s syndrome lead to the cause of hypertension. Although primary aldosteronism is sometimes complicated with preclinical Cushing’s syndrome, concurrence of overt Cushing’s syndrome and primary aldosteronism is very rare. In addition, it has been drawing attention recently that primary aldosteronism is brought about by the presence of aldosterone-producing cell cluster in adjacent adrenal cortex rather than the presence of aldosterone-producing adenoma. Case presentation A 67-year-old Japanese female was referred to our institution due to moon face and central obesity. Based on various clinical findings and data, we diagnosed this subject as overt Cushing’s syndrome and primary aldosteronism. Furthermore, in immunostaining for cytochrome P450 (CYP) 11B1, a cortisol-producing enzyme, diffuse staining was observed in tumorous lesion. Also, in immunostaining for CYP11B2, an aldosterone-producing enzyme, CYP11B2 expression was not observed in tumorous lesion, but strong CYP11B2 expression was observed in adjacent adrenal cortex, indicating the presence of aldosterone-producing cell cluster. Conclusions We should bear in mind the possibility that concurrence of overt Cushing’s syndrome and primary aldosteronism is accompanied by aldosterone-producing cell cluster in adjacent adrenal cortex.