Frequency determination of rare populations by flow cytometry: A hematopoietic stem cell perspective

PerSEveML: a web-based tool to identify persistent biomarker structure for rare events using an integrative machine learning approach

Omics data sets often pose a computational challenge due to their high dimensionality, large size, and non-linear structures. Analyzing these data sets becomes especially daunting in the presence of rare events. Machine learning (ML) methods have gained traction for analyzing rare events, yet there has been limited exploration of bioinformatics tools that integrate ML techniques to comprehend the underlying biology. Expanding upon our previously developed computational framework of an integrative machine learning approach, we introduce PerSEveML, an interactive web-based tool that uses crowd-sourced intelligence to predict rare events and determine feature selection structures. PerSEveML provides a comprehensive overview of the integrative approach through evaluation metrics that help users understand the contribution of individual ML methods to the prediction process. Additionally, PerSEveML calculates entropy and rank scores, which visually organize input features into a persistent structure of selected, unselected, and fluctuating categories that help researchers uncover meaningful hypotheses regarding the underlying biology. We have evaluated PerSEveML on three diverse biologically complex data sets with extremely rare events from small to large scale and have demonstrated its ability to generate valid hypotheses. PerSEveML is available at https://biostats-shinyr.kumc.edu/PerSEveML/ and https://github.com/sreejatadutta/PerSEveML.

PerSEveML: A Web-Based Tool to Identify Persistent Biomarker Structure for Rare Events Using Integrative Machine Learning Approach

Omics datasets often pose a computational challenge due to their high dimensionality, large size, and non-linear structures. Analyzing these datasets becomes especially daunting in the presence of rare events. Machine learning (ML) methods have gained traction for analyzing rare events, yet there remains a limited exploration of bioinformatics tools that integrate ML techniques to comprehend the underlying biology. Expanding upon our previously developed computational framework of an integrative machine learning approach1, we introduce PerSEveML, an interactive web-based that uses crowd-sourced intelligence to predict rare events and determine feature selection structures. PerSEveML provides a comprehensive overview of the integrative approach through evaluation metrics that help users understand the contribution of individual ML methods to the prediction process. Additionally, PerSEveML calculates entropy and rank scores, which visually organize input features into a persistent structure of selected, unselected, and fluctuating categories that help researchers uncover meaningful hypotheses regarding the underlying biology. We have evaluated PerSEveML on three diverse biologically complex data sets with extremely rare events from small to large scale and have demonstrated its ability to generate valid hypotheses. PerSEveML is available at https://biostats-shinyr.kumc.edu/PerSEveML/ and https://github.com/sreejatadutta/PerSEveML.

IRF1 regulates self-renewal and stress responsiveness to support hematopoietic stem cell maintenance

Hematopoietic stem cells (HSCs) are tightly controlled to maintain a balance between blood cell production and self-renewal. While inflammation-related signaling is a critical regulator of HSC activity, the underlying mechanisms and the precise functions of specific factors under steady-state and stress conditions remain incompletely understood. We investigated the role of interferon regulatory factor 1 (IRF1), a transcription factor that is affected by multiple inflammatory stimuli, in HSC regulation. Our findings demonstrate that the loss of IRF1 from mouse HSCs significantly impairs self-renewal, increases stress-induced proliferation, and confers resistance to apoptosis. In addition, given the frequent abnormal expression of IRF1 in leukemia, we explored the potential of IRF1 expression level as a stratification marker for human acute myeloid leukemia. We show that IRF1-based stratification identifies distinct cancer-related signatures in patient subgroups. These findings establish IRF1 as a pivotal HSC controller and provide previously unknown insights into HSC regulation, with potential implications to IRF1 functions in the context of leukemia.

Microfluidics-free single-cell genomics reveals complex central-peripheral immune crosstalk in the mouse brain during peripheral inflammation

Inflammation is a realized detriment to brain health in a growing number of neurological diseases, but querying neuroinflammation in its cellular complexity remains a challenge. This manuscript aims to provide a reliable and accessible strategy for examining the brain's immune system. We compare the efficacy of cell isolation methods in producing ample and pure immune samples from mouse brains. Then, with the high-input single-cell genomics platform PIPseq, we generate a rich neuroimmune dataset containing microglia and many peripheral immune populations. To demonstrate this strategy's utility, we interrogate the well-established model of LPS-induced neuroinflammation with single-cell resolution. We demonstrate the activation of crosstalk between microglia and peripheral phagocytes and highlight the unique contributions of microglia and peripheral immune cells to neuroinflammation. Our approach enables the high-depth evaluation of inflammation in longstanding rodent models of neurological disease to reveal novel insight into the contributions of the immune system to brain health.

Merging and imputation of flow cytometry data: A critical assessment

Although most modern techniques and analysis methods in multiparameter flow cytometry (MFC) allow for increased dimensionality for the characterization and quantification of cell populations, most MFC applications depend on flow cytometers measuring relatively small (<16) numbers of parameters. When more markers than the available parameters need to be acquired, these are commonly distributed over multiple independent measurements that include a backbone of common markers. Several methods have been proposed to impute values for combinations of markers that were not measured simultaneously. These imputation methods are frequently used without proper validation and knowledge of their effects on data analysis. We evaluated the performance of existing imputation software (Infinicyt, CyTOFmerge, CytoBackBone, and cyCombine) in approximating known measured expression data in terms of similarity in visual appearance, cell expression, and gating in different datasets by splitting MFC samples into separate measurements with partially overlapping markers and re-calculating missing marker expression. Out of the assessed packages, CyTOFmerge showed the most accurate approximation of the known expression in terms of similar expression values and concordance with manual gating, with a mean F-score between 0.53 and 0.87 when retrieving cell populations in different datasets. Performance remained inadequate for all methods, with only limited similarity at the cell level. In conclusion, the use of imputed MFC data should take such limitations into account and include independent validation of results to justify conclusions.

CosTaL: an accurate and scalable graph-based clustering algorithm for high-dimensional single-cell data analysis

With the aim of analyzing large-sized multidimensional single-cell datasets, we are describing a method for Cosine-based Tanimoto similarity-refined graph for community detection using Leiden's algorithm (CosTaL). As a graph-based clustering method, CosTaL transforms the cells with high-dimensional features into a weighted k-nearest-neighbor (kNN) graph. The cells are represented by the vertices of the graph, while an edge between two vertices in the graph represents the close relatedness between the two cells. Specifically, CosTaL builds an exact kNN graph using cosine similarity and uses the Tanimoto coefficient as the refining strategy to re-weight the edges in order to improve the effectiveness of clustering. We demonstrate that CosTaL generally achieves equivalent or higher effectiveness scores on seven benchmark cytometry datasets and six single-cell RNA-sequencing datasets using six different evaluation metrics, compared with other state-of-the-art graph-based clustering methods, including PhenoGraph, Scanpy and PARC. As indicated by the combined evaluation metrics, Costal has high efficiency with small datasets and acceptable scalability for large datasets, which is beneficial for large-scale analysis.

Comparative analysis of dimension reduction methods for cytometry by time-of-flight data

While experimental and informatic techniques around single cell sequencing (scRNA-seq) are advanced, research around mass cytometry (CyTOF) data analysis has severely lagged behind. CyTOF data are notably different from scRNA-seq data in many aspects. This calls for the evaluation and development of computational methods specific for CyTOF data. Dimension reduction (DR) is one of the critical steps of single cell data analysis. Here, we benchmark the performances of 21 DR methods on 110 real and 425 synthetic CyTOF samples. We find that less well-known methods like SAUCIE, SQuaD-MDS, and scvis are the overall best performers. In particular, SAUCIE and scvis are well balanced, SQuaD-MDS excels at structure preservation, whereas UMAP has great downstream analysis performance. We also find that t-SNE (along with SQuad-MDS/t-SNE Hybrid) possesses the best local structure preservation. Nevertheless, there is a high level of complementarity between these tools, so the choice of method should depend on the underlying data structure and the analytical needs.

The identification of Pseudomonas aeruginosa persisters using flow cytometry

Pseudomonas aeruginosa persisters are a rare and poorly characterized subpopulation of cells that are responsible for many recurrent infections. The lack of knowledge on the mechanisms that lead to persister cell development is mainly a result of the difficulty in isolating and characterizing this rare population. Flow cytometry is an ideal method for identifying such subpopulations because it allows for high-content single-cell analysis. However, there are fewer established protocols for bacterial flow cytometry compared to mammalian cell work. Herein, we describe and propose a flow cytometry protocol to identify and isolate P. aeruginosa persister cells. Additionally, we show that the percentage of potential persister cells increases with increasing antibiotic concentrations above the MIC.

Developmental cues license megakaryocyte priming in murine hematopoietic stem cells

The fetal-to-adult switch in hematopoietic stem cell (HSC) behavior is characterized by alterations in lineage output and entry into deep quiescence. Here we identify the emergence of megakaryocyte (Mk)-biased HSCs as an event coinciding with this developmental switch. Single-cell chromatin accessibility analysis reveals a ubiquitous acquisition of Mk lineage priming signatures in HSCs during the fetal-to-adult transition. These molecular changes functionally coincide with increased amplitude of early Mk differentiation events after acute inflammatory insult. Importantly, we identify LIN28B, known for its role in promoting fetal-like self-renewal, as an insulator against the establishment of an Mk-biased HSC pool. LIN28B protein is developmentally silenced in the third week of life, and its prolonged expression delays emergency platelet output in young adult mice. We propose that developmental regulation of Mk priming may represent a switch for HSCs to toggle between prioritizing self-renewal in the fetus and increased host protection in postnatal life.

A New Method of Bone Stromal Cell Characterization by Flow Cytometry

The bone microenvironment cellular composition plays an essential role in bone health and is disrupted in bone pathologies, such as osteoporosis, osteoarthritis, and cancer. Flow cytometry protocols for hematopoietic stem cell lineages are well defined and well established. Additionally, a consensus for mesenchymal stem cell flow markers has been developed. However, flow cytometry markers for bone-residing cells-osteoblasts, osteoclasts, and osteocytes-have not been proposed. Here, we describe a novel partial digestion method to separate these cells from the bone matrix and present new markers for enumerating these cells by flow cytometry. We optimized bone digestion and analyzed markers across murine, nonhuman primate, and human bone. The isolation and staining protocols can be used with either cell sorting or flow cytometry. Our method allows for the enumeration and collection of hematopoietic and mesenchymal lineage cells in the bone microenvironment combined with bone-residing stromal cells. Thus, we have established a multi-fluorochrome bone marrow cell-typing methodology. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Partial digestion for murine long bone stromal cell isolation Alternate Protocol 1: Partial digestion for primate vertebrae stromal cell isolation Alternate Protocol 2: Murine vertebrae crushing for bone stromal cell isolation Basic Protocol 2: Staining of bone stromal cells Support Protocol 1: Fluorescence minus one control, isotype control, and antibody titration Basic Protocol 3: Cell sorting of bone stromal cells Alternate Protocol 3: Flow cytometry analysis of bone stromal cells Support Protocol 2: Preparing compensation beads.

The safety of VASApos presumptive adult ovarian stem cells

Isolation and characterization of presumptive human adult ovarian stem cells (OSC) has broken the long standing dogma of the absence of postnatal neo-oogenesis. Human adult OSC have been immunosorted by antibodies reacting against the RNA helicase VASA and have been reported to engraft into appropriate stem cell niches to promote neo-oogenesis. Analysis of published research, however, questions some of the findings on isolation, characterization, in-vitro self-renewal and clinical safety of the presumptive human adult OSC. In the present study, human VASA^pos embryo-fetal primordial germ cells and presumptive adult OSC are shown to share several pluripotency and early germ cell markers not ascertained in the initial characterization of adult OSC. A new hypothesis is made that the restoration of fertility claimed to result from presumptive human adult OSC may be attributed instead to VASA^pos embryo-fetal primordial germ cell remnants in the adult ovary, or alternatively to earlier VASA^neg germ cells generated by in-vitro de-differentiation of the presumptive OSC. The suggested hypotheses have extensive implications for the practice and safety of adult OSC in the development of new treatments aimed at rescuing the ovarian reserve.

Analysis of Radiation-Induced Changes in Cell Cycle and DNA Damage of Murine Hematopoietic Stem Cells by Multi-Color Flow Cytometry

Exposure of bone marrow to genotoxic stress such as ionizing radiation (IR) results in a rapid decline of peripheral blood cells and stimulates entry of the normally quiescent hematopoietic stem cells (HSCs) into the cell cycle to reconstitute the hematopoietic system. While several protocols have employed flow cytometry analysis of bone marrow cells to study changes in specific cell populations with respect to cell cycle proliferation and/or expression of γ-H2AX, a marker of DNA damage, these parameters were examined in separate panels. Here, we describe a flow cytometry-based method specifically designed to examine cell cycle distribution using Ki-67 and FXCycle violet in combination with γ-H2AX in HSCs and hematopoietic progenitor cells (HPCs) within the same sample. This method is very useful, particularly in studies involving genotoxic stresses such as IR, which substantially reduce the absolute numbers of HSCs and HPCs available for staining. Additionally, we describe several important considerations for the analysis of markers of HSCs in irradiated versus unirradiated samples. Examples include the use of fluorescence minus one (FMO) controls, the gating strategy for markers whose expression is typically impacted by IR such as Sca1, tips for staining of intracellular antigens like Ki67, and ensuring the detection of signal from at least 500 events in each gate to ensure robustness of the results. © 2021 Wiley Periodicals LLC. Basic Protocol: Immunostaining protocol for bone marrow mononuclear cells using a multi-fluorophore panel.

Data integration and visualization techniques for post-cytometric analysis of complex datasets

The increasing number of measurable markers and the need to integrate flow cytometry datasets with data generated by other high throughput technologies, require the use of innovative tools, easy enough to be used by people with diverse levels of informatics skills. Flow cytometry analysis software has principally been designed for single sample analysis and it does not cover all the successive analysis steps such as integration with metadata and complex visualization. Here, we illustrated the use of data integration and visualization tools generally used in the business sector to analyze datasets generated by mass and flow cytometry. We selected a study that used mass cytometry to characterize immune cells in lung adenocarcinoma and a second study that used flow cytometry to characterize the expression signature of CD markers on human immune cells. These two examples showed the effectiveness of these tools in the analysis of cytometry data and the possibility to expand their use in any field of biology.

Flow cytometry: principles, applications and recent advances

Flow cytometry (FCM) is a sophisticated technique that works on the principle of light scattering and fluorescence emission by the specific fluorescent probe-labeled cells as they pass through a laser beam. It offers several unique advantages as it allows fast, relatively quantitative, multiparametric analysis of cell populations at the single cell level. In addition, it also enables physical sorting of the cells to separate the subpopulations based on different parameters. In this constantly evolving field, innovative technologies such as imaging FCM, mass cytometry and Raman FCM are being developed in order to address limitations of traditional FCM. This review explains the general principles, main applications and recent advances in the field of FCM.

SCHNEL: scalable clustering of high dimensional single-cell data

MOTIVATION

Single cell data measures multiple cellular markers at the single-cell level for thousands to millions of cells. Identification of distinct cell populations is a key step for further biological understanding, usually performed by clustering this data. Dimensionality reduction based clustering tools are either not scalable to large datasets containing millions of cells, or not fully automated requiring an initial manual estimation of the number of clusters. Graph clustering tools provide automated and reliable clustering for single cell data, but suffer heavily from scalability to large datasets.

RESULTS

We developed SCHNEL, a scalable, reliable and automated clustering tool for high-dimensional single-cell data. SCHNEL transforms large high-dimensional data to a hierarchy of datasets containing subsets of data points following the original data manifold. The novel approach of SCHNEL combines this hierarchical representation of the data with graph clustering, making graph clustering scalable to millions of cells. Using seven different cytometry datasets, SCHNEL outperformed three popular clustering tools for cytometry data, and was able to produce meaningful clustering results for datasets of 3.5 and 17.2 million cells within workable time frames. In addition, we show that SCHNEL is a general clustering tool by applying it to single-cell RNA sequencing data, as well as a popular machine learning benchmark dataset MNIST.

AVAILABILITY AND IMPLEMENTATION

Implementation is available on GitHub (https://github.com/biovault/SCHNELpy). All datasets used in this study are publicly available.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identification of stem cells from large cell populations with topological scoring

Machine learning and topological analysis methods are becoming increasingly used on various large-scale omics datasets. Modern high dimensional flow cytometry data sets share many features with other omics datasets like genomics and proteomics. For example, genomics or proteomics datasets can be sparse and have high dimensionality, and flow cytometry datasets can also share these features. This makes flow cytometry data potentially a suitable candidate for employing machine learning and topological scoring strategies, for example, to gain novel insights into patterns within the data. We have previously developed a Topological Score (TopS) and implemented it for the analysis of quantitative protein interaction network datasets. Here we show that TopS approach for large scale data analysis is applicable to the analysis of a previously described flow cytometry sorted human hematopoietic stem cell dataset. We demonstrate that TopS is capable of effectively sorting this dataset into cell populations and identify rare cell populations. We demonstrate the utility of TopS when coupled with multiple approaches including topological data analysis, X-shift clustering, and t-Distributed Stochastic Neighbor Embedding (t-SNE). Our results suggest that TopS could be effectively used to analyze large scale flow cytometry datasets to find rare cell populations.

PARC: ultrafast and accurate clustering of phenotypic data of millions of single cells

MOTIVATION

New single-cell technologies continue to fuel the explosive growth in the scale of heterogeneous single-cell data. However, existing computational methods are inadequately scalable to large datasets and therefore cannot uncover the complex cellular heterogeneity.

RESULTS

We introduce a highly scalable graph-based clustering algorithm PARC-Phenotyping by Accelerated Refined Community-partitioning-for large-scale, high-dimensional single-cell data (>1 million cells). Using large single-cell flow and mass cytometry, RNA-seq and imaging-based biophysical data, we demonstrate that PARC consistently outperforms state-of-the-art clustering algorithms without subsampling of cells, including Phenograph, FlowSOM and Flock, in terms of both speed and ability to robustly detect rare cell populations. For example, PARC can cluster a single-cell dataset of 1.1 million cells within 13 min, compared with >2 h for the next fastest graph-clustering algorithm. Our work presents a scalable algorithm to cope with increasingly large-scale single-cell analysis.

AVAILABILITY AND IMPLEMENTATION

https://github.com/ShobiStassen/PARC.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Early chronic low-level lead exposure reduced C-C chemokine receptor 7 in hippocampal microglia

Chronic low-level lead exposure alters cognitive function in young children however the mechanisms mediating these deficits in the brain are not known. Previous studies in our laboratory showed that early lead exposure reduced the number of microglial cells in hippocampus/dentate gyrus of C57BL/6 J mice. In the current study, C-C chemokine receptor 7 (CCR7) and major histocompatibility complex II (MHC II) were examined to investigate whether these neuroimmune factors which are known to trigger cell migration and antigen presentation, were altered by early chronic lead exposure. Thirty-six C57BL/6 J male mice were exposed to 0 ppm (controls, n = 12), 30 ppm (low-dose, n = 12), or 430 ppm (higher-dose, n = 12) of lead acetate via dams' milk from postnatal day (PND) 0 to 28. Flow cytometry was used to quantify cell types and cell surface expression of MHC II and CCR7 in hippocampal and whole brain microglia. Non-parametric independent samples median tests were used to test for statistically significant differences between groups. As compared to controls, CCR7 in hippocampal microglia was decreased in the low-dose group, measured as geometric mean fluorescence intensity (GMFI); in the higher-dose group CCR7+MHC II- hippocampal microglia were decreased. Further analyses revealed that the higher-dose group had decreased percentage of CCR7+MHC II- hippocampal macrophages as compared to controls but increased MHC II levels in CCR7+MHC II+ hippocampal macrophages as compared to controls. It was also noted that lead exposure disrupted the balance of MHC II and/or CCR7 in lead exposed animals. Reduced CCR7 in hippocampal microglia might alter the neuroimmune environment in hippocampi of lead exposed animals. Additional studies are needed to test this possibility.

Rigor and Reproducibility of Cytometry Practices for Immuno-Oncology: A multifaceted challenge

The rapid advancement of immunotherapy strategies has created a need for technologies that can reliably and reproducibly identify rare populations, detect subtle changes in modulatory signals, and assess antigenic expression patterns that are time-sensitive. Accomplishing these tasks requires careful planning and the employment of tools that provide greater sensitivity and specificity without demanding extensive time. Flow Cytometry has earned its place as a preferred analysis platform. This technology offers a flexible path to the interrogation of protein expression patterns and detection of functional properties in cell populations of interest. Mass Cytometry is a newcomer technology that has generated significant interest in the field. By incorporating mass spectrometry analysis to the traditional principles of flow cytometry, this innovative tool promises to significantly expand the ability to detect multiple proteins on a single cell. The use of these technologies in a manner that is consistent and reproducible through multiple sample sets demands careful attention to experiment design, reagent selection, and instrumentation. Whether applying flow or mass cytometry, reaching successful, reliable results involves many factors. Sample preparation, antibody titrations, and appropriate controls are major biological considerations that impact cytometric analysis. Additionally, instrument voltages, lasers, and run quality assessments are essential for ensuring comparability and reproducibility between analyses. In this article, we aim to discuss the critical aspects that impact flow cytometry, and will touch on important considerations for mass cytometry as well. Focusing on their relevance to immunotherapy studies, we will address the importance of appropriate sample processing and will discuss how selection of suitable panels, controls, and antibodies must follow a carefully designed plan. We will also comment on how educated use of instrumentation plays a significant role in the reliability and reproducibility of results.Through this work, we hope to contribute to the effort toward establishing higher standards for rigor and reproducibility of cytometry practices by researchers, operators, and general cytometry users employing cytometry-based assays in their work. © 2019 International Society for Advancement of Cytometry.

HDCytoData: Collection of high-dimensional cytometry benchmark datasets in Bioconductor object formats

Benchmarking is a crucial step during computational analysis and method development. Recently, a number of new methods have been developed for analyzing high-dimensional cytometry data. However, it can be difficult for analysts and developers to find and access well-characterized benchmark datasets. Here, we present HDCytoData, a Bioconductor package providing streamlined access to several publicly available high-dimensional cytometry benchmark datasets. The package is designed to be extensible, allowing new datasets to be contributed by ourselves or other researchers in the future. Currently, the package includes a set of experimental and semi-simulated datasets, which have been used in our previous work to evaluate methods for clustering and differential analyses. Datasets are formatted into standard SummarizedExperiment and flowSet Bioconductor object formats, which include complete metadata within the objects. Access is provided through Bioconductor's ExperimentHub interface. The package is freely available from http://bioconductor.org/packages/HDCytoData.

HDCytoData: Collection of high-dimensional cytometry benchmark datasets in Bioconductor object formats

Benchmarking is a crucial step during computational analysis and method development. Recently, a number of new methods have been developed for analyzing high-dimensional cytometry data. However, it can be difficult for analysts and developers to find and access well-characterized benchmark datasets. Here, we present HDCytoData, a Bioconductor package providing streamlined access to several publicly available high-dimensional cytometry benchmark datasets. The package is designed to be extensible, allowing new datasets to be contributed by ourselves or other researchers in the future. Currently, the package includes a set of experimental and semi-simulated datasets, which have been used in our previous work to evaluate methods for clustering and differential analyses. Datasets are formatted into standard SummarizedExperiment and flowSet Bioconductor object formats, which include complete metadata within the objects. Access is provided through Bioconductor's ExperimentHub interface. The package is freely available from http://bioconductor.org/packages/HDCytoData.

Flow Cytometry Data Preparation Guidelines for Improved Automated Phenotypic Analysis

Advances in flow cytometry (FCM) increasingly demand adoption of computational analysis tools to tackle the ever-growing data dimensionality. In this study, we tested different data input modes to evaluate how cytometry acquisition configuration and data compensation procedures affect the performance of unsupervised phenotyping tools. An analysis workflow was set up and tested for the detection of changes in reference bead subsets and in a rare subpopulation of murine lymph node CD103⁺ dendritic cells acquired by conventional or spectral cytometry. Raw spectral data or pseudospectral data acquired with the full set of available detectors by conventional cytometry consistently outperformed datasets acquired and compensated according to FCM standards. Our results thus challenge the paradigm of one-fluorochrome/one-parameter acquisition in FCM for unsupervised cluster-based analysis. Instead, we propose to configure instrument acquisition to use all available fluorescence detectors and to avoid integration and compensation procedures, thereby using raw spectral or pseudospectral data for improved automated phenotypic analysis.

Immunophenotypic Identification of Early Myeloerythroid Development

Myeloerythroid-restricted precursor cells, derived from multipotent hematopoietic stem cells, give rise to mature cells of the granulocyte, monocyte, erythroid, and/or thrombocytic lineages. High-resolution profiling of the developmental stages, from hematopoietic stem cells to mature progeny, is important to be able to study and understand the underlying mechanisms that guide various cell fate decisions. Also, this approach opens for greater insights into pathogenic events such as leukemia, diseases that are most often characterized by halted differentiation at defined immature precursor levels. In this chapter, we provide protocols and discuss approaches concerning the analysis and purification of immature myeloerythroid lineages by multiparameter flow cytometry. A wealth of literature has demonstrated the feasibility of similar approaches also for the human system. However, in this chapter, we focus on the identification of bone marrow cells derived from C57BL/6 mice, in which flow cytometry-based immunophenotypic applications have been most widely developed. This should allow also for its application in genetically modified models on this background. For maximal reproducibility, all protocols described have been established using reagents from commercial vendors to be analyzed on a flow cytometer with factory standard configuration.

Competitive Transplants to Evaluate Hematopoietic Stem Cell Fitness

The gold standard definition of a hematopoietic stem cell (HSC) is a cell that when transferred into an irradiated recipient will have the ability to reestablish blood cell production for the lifespan of the recipient. This protocol explains how to set up a functional assay to compare the HSC capacities of two different populations of cells, such as bone marrow from mice of two different genotypes, and how to analyze the recipient mice by flow cytometry. The protocol uses HSC equivalents rather than cell sorting for standardization and discusses the advantages and disadvantages of both approaches. We further discuss different variations to the basic protocol, including serial transplants, limiting dilution assays, homing assays and non-competitive transplants, including the advantages and preferred uses of these varied approaches. These assays are central for the study of HSC function and could be used not only for the investigation of fundamental HSC intrinsic aspects of biology but also for the development of preclinical assays for bone marrow transplant and HSC expansion in culture.

Gene expression variability in clonal populations: Causes and consequences

During the last decade it has been shown that among cell variation in gene expression plays an important role within clonal populations. Here, we provide an overview of the different mechanisms contributing to gene expression variability in clonal populations. These are ranging from inherent variations in the biochemical process of gene expression itself, such as intrinsic noise, extrinsic noise and bistability to individual responses to variations in the local micro-environment, a phenomenon called phenotypic plasticity. Also genotypic variations caused by clonal evolution and phase variation can contribute to gene expression variability. Consequently, gene expression studies need to take these fluctuations in expression into account. However, frequently used techniques for expression quantification, such as microarrays, RNA sequencing, quantitative PCR and gene reporter fusions classically determine the population average of gene expression. Here, we discuss how these techniques can be adapted towards single cell analysis by integration with single cell isolation, RNA amplification and microscopy. Alternatively more qualitative selection-based techniques, such as mutant screenings, in vivo expression technology (IVET) and recombination-based IVET (RIVET) can be applied for detection of genes expressed only within a subpopulation. Finally, differential fluorescence induction (DFI), a protocol specially designed for single cell expression is discussed.

Multiparameter Phenotyping of Human PBMCs Using Mass Cytometry

The standard for single-cell analysis of phenotype and function in recent decades has been fluorescence flow cytometry. Mass cytometry is a newer technology that uses heavy metal ions, rather than fluorochromes, as labels for probes such as antibodies. The binding of these ion-labeled probes to cells is quantitated by mass spectrometry. This greatly increases the number of phenotypic and functional markers that can be probed simultaneously. Here, we review topics that must be considered when adapting existing flow cytometry panels to mass cytometry analysis. We present a protocol and representative panels for surface phenotyping and intracellular cytokine staining (ICS) assays.