Statistical classification of multivariate flow cytometry data analyzed by manual gating: stem, progenitor, and epithelial marker expression in nonsmall cell lung cancer and normal lung

An immune risk score predicts progression-free survival of melanoma patients in South China receiving anti-PD-1 inhibitor therapy-a retrospective cohort study examining 66 circulating immune cell subsets

Introduction

Immune checkpoint blockade inhibitor (ICI) therapy offers significant survival benefits for malignant melanoma. However, some patients were observed to be in disease progression after the first few treatment cycles. As such, it is urgent to find convenient and accessible indicators that assess whether patients can benefit from ICI therapy.

Methods

In the training cohort, flow cytometry was used to determine the absolute values of 66 immune cell subsets in the peripheral blood of melanoma patients (n=29) before treatment with anti-PD-1 inhibitors. The least absolute shrinkage and selection operator (LASSO) Cox regression model was followed for the efficacy of each subset in predicting progression-free survival. Then we validated the performance of the selected model in validation cohorts (n=20), and developed a nomogram for clinical use.

Results

A prognostic immune risk score composed of CD1c⁺ dendritic cells and three subsets of T cells (CD8⁺CD28⁺, CD3⁺TCRab⁺HLA-DR⁺, CD3⁺TCRgd⁺HLA-DR⁺) with a higher prognostic power than individual features (AUC = 0.825). Using this model, patients in the training cohort were divided into high- and low-risk groups with significant differences in mean progression-free survival (3.6 vs. 12.3 months), including disease control rate (41.2% vs. 91.7%), and objective response rate (17.6% vs. 41.6%). Integrating four-immune cell-subset based classifiers and three clinicopathologic risk factors can help to predict which patients might benefit from anti-PD-1 antibody inhibitors and remind potential non-responders to pursue effective treatment options in a timely way.

Conclusions

The prognostic immune risk score including the innate immune and adaptive immune cell populations could provide an accurate prediction efficacy in malignant melanoma patients with ICI therapy.

Anthrax Edema and Lethal Toxins Differentially Target Human Lung and Blood Phagocytes

Bacillus anthracis, the causative agent of inhalation anthrax, is a serious concern as a bioterrorism weapon. The vegetative form produces two exotoxins: Lethal toxin (LT) and edema toxin (ET). We recently characterized and compared six human airway and alveolar-resident phagocyte (AARP) subsets at the transcriptional and functional levels. In this study, we examined the effects of LT and ET on these subsets and human leukocytes. AARPs and leukocytes do not express high levels of the toxin receptors, tumor endothelium marker-8 (TEM8) and capillary morphogenesis protein-2 (CMG2). Less than 20% expressed surface TEM8, while less than 15% expressed CMG2. All cell types bound or internalized protective antigen, the common component of the two toxins, in a dose-dependent manner. Most protective antigen was likely internalized via macropinocytosis. Cells were not sensitive to LT-induced apoptosis or necrosis at concentrations up to 1000 ng/mL. However, toxin exposure inhibited B. anthracis spore internalization. This inhibition was driven primarily by ET in AARPs and LT in leukocytes. These results support a model of inhalation anthrax in which spores germinate and produce toxins. ET inhibits pathogen phagocytosis by AARPs, allowing alveolar escape. In late-stage disease, LT inhibits phagocytosis by leukocytes, allowing bacterial replication in the bloodstream.

Self-Learning Microfluidic Platform for Single-Cell Imaging and Classification in Flow

Single-cell analysis commonly requires the confinement of cell suspensions in an analysis chamber or the precise positioning of single cells in small channels. Hydrodynamic flow focusing has been broadly utilized to achieve stream confinement in microchannels for such applications. As imaging flow cytometry gains popularity, the need for imaging-compatible microfluidic devices that allow for precise confinement of single cells in small volumes becomes increasingly important. At the same time, high-throughput single-cell imaging of cell populations produces vast amounts of complex data, which gives rise to the need for versatile algorithms for image analysis. In this work, we present a microfluidics-based platform for single-cell imaging in-flow and subsequent image analysis using variational autoencoders for unsupervised characterization of cellular mixtures. We use simple and robust Y-shaped microfluidic devices and demonstrate precise 3D particle confinement towards the microscope slide for high-resolution imaging. To demonstrate applicability, we use these devices to confine heterogeneous mixtures of yeast species, brightfield-image them in-flow and demonstrate fully unsupervised, as well as few-shot classification of single-cell images with 88% accuracy.

Of Cytometry, Stem Cells and Fountain of Youth

Outlined are advances of cytometry applications to identify and sort stem cells, of laser scanning cytometry and ImageStream imaging instrumentation to further analyze morphometry of these cells, and of mass cytometry to classify a multitude of cellular markers in large cell populations. Reviewed are different types of stem cells, including potential candidates for cancer stem cells, with respect to their "stemness", and other characteristics. Appraised is further progress in identification and isolation of the "very small embryonic-like stem cells" (VSELs) and their autogenous transplantation for tissue repair and geroprotection. Also assessed is a function of hyaluronic acid, the major stem cells niche component, as a guardian and controller of stem cells. Briefly appraised are recent advances and challenges in the application of stem cells in regenerative medicine and oncology and their future role in different disciplines of medicine, including geriatrics.

Flow Cytometric Single-Cell Identification of Populations in Synthetic Bacterial Communities

Bacterial cells can be characterized in terms of their cell properties using flow cytometry. Flow cytometry is able to deliver multiparametric measurements of up to 50,000 cells per second. However, there has not yet been a thorough survey concerning the identification of the population to which bacterial single cells belong based on flow cytometry data. This paper not only aims to assess the quality of flow cytometry data when measuring bacterial populations, but also suggests an alternative approach for analyzing synthetic microbial communities. We created so-called in silico communities, which allow us to explore the possibilities of bacterial flow cytometry data using supervised machine learning techniques. We can identify single cells with an accuracy >90% for more than half of the communities consisting out of two bacterial populations. In order to assess to what extent an in silico community is representative for its synthetic counterpart, we created so-called abundance gradients, a combination of synthetic (i.e., in vitro) communities containing two bacterial populations in varying abundances. By showing that we are able to retrieve an abundance gradient using a combination of in silico communities and supervised machine learning techniques, we argue that in silico communities form a viable representation for synthetic bacterial communities, opening up new opportunities for the analysis of synthetic communities and bacterial flow cytometry data in general.

Node-pore sensing enables label-free surface-marker profiling of single cells

Flow cytometry is a ubiquitous, multiparametric method for characterizing cellular populations. However, this method can grow increasingly complex with the number of proteins that need to be screened simultaneously: spectral emission overlap of fluorophores and the subsequent need for compensation, lengthy sample preparation, and multiple control tests that need to be performed separately must all be considered. These factors lead to increased costs, and consequently, flow cytometry is performed in core facilities with a dedicated technician operating the instrument. Here, we describe a low-cost, label-free microfluidic method that can determine the phenotypic profiles of single cells. Our method employs Node-Pore Sensing to measure the transit times of cells as they interact with a series of different antibodies, each corresponding to a specific cell-surface antigen, that have been functionalized in a single microfluidic channel. We demonstrate the capabilities of our method not only by screening two acute promyelocytic leukemia human cells lines (NB4 and AP-1060) for myeloid antigens, CD13, CD14, CD15, and CD33, simultaneously, but also by distinguishing a mixture of cells of similar size—AP-1060 and NALM-1—based on surface markers CD13 and HLA-DR. Furthermore, we show that our method can screen complex subpopulations in clinical samples: we successfully identified the blast population in primary human bone marrow samples from patients with acute myeloid leukemia and screened these cells for CD13, CD34, and HLA-DR. We show that our label-free method is an affordable, highly sensitive, and user-friendly technology that has the potential to transform cellular screening at the benchside.

Flow cytometric determination of stem/progenitor content in epithelial tissues: an example from nonsmall lung cancer and normal lung

Single cell analysis and cell sorting has enabled the study of development, growth, differentiation, repair and maintenance of "liquid" tissues and their cancers. The application of these methods to solid tissues is equally promising, but several unique technical challenges must be addressed. This report illustrates the application of multidimensional flow cytometry to the identification of candidate stem/progenitor populations in non-small cell lung cancer and paired normal lung tissue. Seventeen paired tumor/normal lung samples were collected at the time of surgical excision and processed immediately. Tissues were mechanically and enzymatically dissociated into single cell suspension and stained with a panel of antibodies used for negative gating (CD45, CD14, CD33, glycophorin A), identification of epithelial cells (intracellular cytokeratin), and detection of stem/progenitor markers (CD44, CD90, CD117, CD133). DAPI was added to measure DNA content. Formalin fixed paraffin embedded tissue samples were stained with key markers (cytokeratin, CD117, DAPI) for immunofluorescent tissue localization of populations detected by flow cytometry. Disaggregated tumor and lung preparations contained a high proportion of events that would interfere with analysis, were they not eliminated by logical gating. We demonstrate how inclusion of doublets, events with hypodiploid DNA, and cytokeratin+ events also staining for hematopoietic markers reduces the ability to quantify epithelial cells and their precursors. Using the lung cancer/normal lung data set, we present an approach to multidimensional data analysis that consists of artifact removal, identification of classes of cells to be studied further (classifiers) and the measurement of outcome variables on these cell classes. The results of bivariate analysis show a striking similarity between the expression of stem/progenitor markers on lung tumor and adjacent tumor-free lung.