Identifying the Trends of Urinary microRNAs within Extracellular Vesicles for Esophageal Cancer.

Background: The advancement of multidisciplinary treatment has increased the need to develop tests to monitor tumor burden during treatment. We herein analyzed urinary microRNAs within extracellular vesicles from patients with esophageal squamous cell carcinoma (ESCC) and normal individuals using a microarray. Methods: Patients with advanced ESCC who underwent esophagectomy (A), endoscopic submucosal resection (ESD) (B), and healthy donors (C) were included. Based on microRNA expression among the groups (Analysis 1), microRNAs with significant differences between groups A and C were selected (Analysis 2). Of these candidates, microRNAs in which the change between A and C was consistent with the change between B and C were selected for downstream analysis (Analysis 3). Finally, microRNA expression was validated in patients with recurrence from A (exploratory analysis). Results: For analysis 1, 205 microRNAs were selected. For Analyses 2 and 3, the changes in 18 microRNAs were consistent with changes in tumor burden as determined by clinical imaging and pathological findings. The AUC for the detection of ESCC using 18 microRNAs was 0.72. In exploratory analysis, three of eighteen microRNAs exhibited a concordant trend with recurrence. Conclusions: The current study identified the urinary microRNAs which were significantly expressed in ESCC patients. Validation study is warranted to evaluate whether these microRNAs could reflect tumor burden during multidisciplinary treatment for ESCC.

The plasma membrane of focal adhesions has a high content of cholesterol and phosphatidylcholine with saturated acyl chains.

Cellular functions, such as differentiation and migration, are regulated by the extracellular microenvironment, including the extracellular matrix (ECM). Cells adhere to ECM through focal adhesions (FAs) and sense the surrounding microenvironments. Although FA proteins have been actively investigated, little is known about the lipids in the plasma membrane at FAs. In this study, we examine the lipid composition at FAs with imaging and biochemical approaches. Using the cholesterol-specific probe D4 with total internal reflection fluorescence microscopy and super-resolution microscopy, we show an enrichment of cholesterol at FAs simultaneously with FA assembly. Furthermore, we establish a method to isolate the lipid from FA-rich fractions, and biochemical quantification of the lipids reveals that there is a higher content of cholesterol and phosphatidylcholine with saturated fatty acid chains in the lipids of the FA-rich fraction than in either the plasma membrane fraction or the whole-cell membrane. These results demonstrate that plasma membrane at FAs has a locally distinct lipid composition compared to the bulk plasma membrane.

A bifurcation concept for B-lymphoid/plasmacytoid dendritic cells with largely fluctuating transcriptome dynamics.

Hematopoiesis was considered a hierarchical stepwise process but was revised to a continuous process following single-cell RNA sequencing. However, the uncertainty or fluctuation of single-cell transcriptome dynamics during differentiation was not considered, and the dendritic cell (DC) pathway in the lymphoid context remains unclear. Here, we identify human B-plasmacytoid DC (pDC) bifurcation as large fluctuating transcriptome dynamics in the putative B/NK progenitor region by dry and wet methods. By converting splicing kinetics into diffusion dynamics in a deep generative model, our original computational methodology reveals strong fluctuation at B/pDC bifurcation in IL-7Rα+ regions, and LFA-1 fluctuates positively in the pDC direction at the bifurcation. These expectancies are validated by the presence of B/pDC progenitors in the IL-7Rα+ fraction and preferential expression of LFA-1 in pDC-biased progenitors with a niche-like culture system. We provide a model of fluctuation-based differentiation, which reconciles continuous and discrete models and is applicable to other developmental systems.

Depletion of central memory CD8+ T cells might impede the antitumor therapeutic effect of Mogamulizumab.

Regulatory T (Treg) cells are important negative regulators of immune homeostasis, but in cancers they tone down the anti-tumor immune response. They are distinguished by high expression levels of the chemokine receptor CCR4, hence their targeting by the anti-CCR4 monoclonal antibody mogamulizumab holds therapeutic promise. Here we show that despite a significant reduction in peripheral effector Treg cells, clinical responses are minimal in a cohort of patients with advanced CCR4-negative solid cancer in a phase Ib study (NCT01929486). Comprehensive immune-monitoring reveals that the abundance of CCR4-expressing central memory CD8+ T cells that are known to play roles in the antitumor immune response is reduced. In long survivors, characterised by lower CCR4 expression in their central memory CD8+ T cells possessed and/or NK cells with an exhausted phenotype, cell numbers are eventually maintained. Our study thus shows that mogamulizumab doses that are currently administered to patients in clinical studies may not differentiate between targeting effector Treg cells and central memory CD8+ T cells, and dosage refinement might be necessary to avoid depletion of effector components during immune therapy.

A mixture-of-experts deep generative model for integrated analysis of single-cell multiomics data.

The recent development of single-cell multiomics analysis has enabled simultaneous detection of multiple traits at the single-cell level, providing deeper insights into cellular phenotypes and functions in diverse tissues. However, currently, it is challenging to infer the joint representations and learn relationships among multiple modalities from complex multimodal single-cell data. Here, we present scMM, a novel deep generative model-based framework for the extraction of interpretable joint representations and crossmodal generation. scMM addresses the complexity of data by leveraging a mixture-of-experts multimodal variational autoencoder. The pseudocell generation strategy of scMM compensates for the limited interpretability of deep learning models, and the proposed approach experimentally discovered multimodal regulatory programs associated with latent dimensions. Analysis of recently produced datasets validated that scMM facilitates high-resolution clustering with rich interpretability. Furthermore, we show that crossmodal generation by scMM leads to more precise prediction and data integration compared with the state-of-the-art and conventional approaches.

CYBERTRACK2.0: zero-inflated model-based cell clustering and population tracking method for longitudinal mass cytometry data.

SUMMARY: Recent advancements in high-dimensional single-cell technologies, such as mass cytometry, enable longitudinal experiments to track dynamics of cell populations and identify change points where the proportions vary significantly. However, current research is limited by the lack of tools specialized for analyzing longitudinal mass cytometry data. In order to infer cell population dynamics from such data, we developed a statistical framework named CYBERTRACK2.0. The framework's analytic performance was validated against synthetic and real data, showing that its results are consistent with previous research. AVAILABILITY AND IMPLEMENTATION: CYBERTRACK2.0 is available at https://github.com/kodaim1115/CYBERTRACK2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Model-based clustering for flow and mass cytometry data with clinical information.

BACKGROUND: High-dimensional flow cytometry and mass cytometry allow systemic-level characterization of more than 10 protein profiles at single-cell resolution and provide a much broader landscape in many biological applications, such as disease diagnosis and prediction of clinical outcome. When associating clinical information with cytometry data, traditional approaches require two distinct steps for identification of cell populations and statistical test to determine whether the difference between two population proportions is significant. These two-step approaches can lead to information loss and analysis bias. RESULTS: We propose a novel statistical framework, called LAMBDA (Latent Allocation Model with Bayesian Data Analysis), for simultaneous identification of unknown cell populations and discovery of associations between these populations and clinical information. LAMBDA uses specified probabilistic models designed for modeling the different distribution information for flow or mass cytometry data, respectively. We use a zero-inflated distribution for the mass cytometry data based the characteristics of the data. A simulation study confirms the usefulness of this model by evaluating the accuracy of the estimated parameters. We also demonstrate that LAMBDA can identify associations between cell populations and their clinical outcomes by analyzing real data. LAMBDA is implemented in R and is available from GitHub ( https://github.com/abikoushi/lambda ).

Model-based cell clustering and population tracking for time-series flow cytometry data.

BACKGROUND: Modern flow cytometry technology has enabled the simultaneous analysis of multiple cell markers at the single-cell level, and it is widely used in a broad field of research. The detection of cell populations in flow cytometry data has long been dependent on "manual gating" by visual inspection. Recently, numerous software have been developed for automatic, computationally guided detection of cell populations; however, they are not designed for time-series flow cytometry data. Time-series flow cytometry data are indispensable for investigating the dynamics of cell populations that could not be elucidated by static time-point analysis. Therefore, there is a great need for tools to systematically analyze time-series flow cytometry data. RESULTS: We propose a simple and efficient statistical framework, named CYBERTRACK (CYtometry-Based Estimation and Reasoning for TRACKing cell populations), to perform clustering and cell population tracking for time-series flow cytometry data. CYBERTRACK assumes that flow cytometry data are generated from a multivariate Gaussian mixture distribution with its mixture proportion at the current time dependent on that at a previous timepoint. Using simulation data, we evaluate the performance of CYBERTRACK when estimating parameters for a multivariate Gaussian mixture distribution, tracking time-dependent transitions of mixture proportions, and detecting change-points in the overall mixture proportion. The CYBERTRACK performance is validated using two real flow cytometry datasets, which demonstrate that the population dynamics detected by CYBERTRACK are consistent with our prior knowledge of lymphocyte behavior. CONCLUSIONS: Our results indicate that CYBERTRACK offers better understandings of time-dependent cell population dynamics to cytometry users by systematically analyzing time-series flow cytometry data.

Maturation of hematopoietic stem cells from prehematopoietic stem cells is accompanied by up-regulation of PD-L1.

Hematopoietic stem cells (HSCs) mature from pre-HSCs that originate in the major arteries of the embryo. To identify HSCs from in vitro sources, it will be necessary to refine markers of HSCs matured ex vivo. We purified and compared the transcriptomes of pre-HSCs, HSCs matured ex vivo, and fetal liver HSCs. We found that HSC maturation in vivo or ex vivo is accompanied by the down-regulation of genes involved in embryonic development and vasculogenesis, and up-regulation of genes involved in hematopoietic organ development, lymphoid development, and immune responses. Ex vivo matured HSCs more closely resemble fetal liver HSCs than pre-HSCs, but are not their molecular equivalents. We show that ex vivo-matured and fetal liver HSCs express programmed death ligand 1 (PD-L1). PD-L1 does not mark all pre-HSCs, but cell surface PD-L1 was present on HSCs matured ex vivo. PD-L1 signaling is not required for engraftment of embryonic HSCs. Hence, up-regulation of PD-L1 is a correlate of, but not a requirement for, HSC maturation.