Parallel analysis of tri-molecular biosynthesis with cell identity and function in single cells

CytoPheno: Automated descriptive cell type naming in flow and mass cytometry

Advances in cytometry have led to increases in the number of cellular markers that are routinely measured. The resulting complexity of the data has prompted a shift from manual to automated analysis methods. Currently, numerous unsupervised methods are available to cluster cells based on marker expression values. However, phenotyping the resulting clusters is typically not part of the automated process. Manually identifying both marker definitions (e.g. CD4+, CCR7+, CD45RA+, CD19-) and descriptive cell type names (e.g. naïve CD4+ T cells) based on marker expression values can be time-consuming, subjective, and error-prone. In this work we propose an algorithm that addresses these problems through the creation of an automated tool, CytoPheno, that assigns marker definitions and cell type names to unidentified clusters. First, post-clustered expression data undergoes per-marker calculations to assign markers as positive or negative. Next, marker names undergo a standardization process to match to Protein Ontology identifier terms. Finally, marker descriptions are matched to cell type names within the Cell Ontology. Each part of the tool was tested with benchmark data to demonstrate performance. Additionally, the tool is encompassed in a graphical user interface (R Shiny) to increase user accessibility and interpretability. Overall, CytoPheno can aid researchers in timely and unbiased phenotyping of post-clustered cytometry data.

Single cell suppression profiling of human regulatory T cells

Regulatory T cells (Treg) play an important role in regulating immune homeostasis in health and disease. Traditionally their suppressive function has been assayed by mixing purified cell populations, which does not provide an accurate picture of a physiologically relevant response. To overcome this limitation, we here develop 'single cell suppression profiling of human Tregs' (scSPOT). scSPOT uses a 52-marker CyTOF panel, a cell division detection algorithm, and a whole PBMC system to assess the effect of Tregs on all other cell types simultaneously. In this head-to-head comparison, we find Tregs having the clearest suppressive effects on effector memory CD8 T cells through partial division arrest, cell cycle inhibition, and effector molecule downregulation. Additionally, scSPOT identifies a Treg phenotypic split previously observed in viral infection and propose modes of action by the FDA-approved drugs Ipilimumab and Tazemetostat. scSPOT is thus scalable, robust, widely applicable, and may be used to better understand Treg immunobiology and screen for therapeutic compounds.

Atypical and non-classical CD45RBlo memory B cells are the majority of circulating SARS-CoV-2 specific B cells following mRNA vaccination or COVID-19

Resting memory B cells can be divided into classical or atypical groups, but the heterogenous marker expression on activated memory B cells makes similar classification difficult. Here, by longitudinal analysis of mass cytometry and CITE-seq data from cohorts with COVID-19, bacterial sepsis, or BNT162b2 mRNA vaccine, we observe that resting B cell memory consist of classical CD45RB+ memory and CD45RBlo memory, of which the latter contains of two distinct groups of CD11c+ atypical and CD23+ non-classical memory cells. CD45RB levels remain stable in these cells after activation, thereby enabling the tracking of activated B cells and plasmablasts derived from either CD45RB+ or CD45RBlo memory B cells. Moreover, in both COVID-19 patients and mRNA vaccination, CD45RBlo B cells formed the majority of SARS-CoV2 specific memory B cells and correlated with serum antibodies, while CD45RB+ memory are activated by bacterial sepsis. Our results thus identify that stably expressed CD45RB levels can be exploited to trace resting memory B cells and their activated progeny, and suggest that atypical and non-classical CD45RBlo memory B cells contribute to SARS-CoV-2 infection and vaccination.

Terminal deoxynucleotidyl transferase and CD84 identify human multi-potent lymphoid progenitors

Lymphoid specification in human hematopoietic progenitors is not fully understood. To better associate lymphoid identity with protein-level cell features, we conduct a highly multiplexed single-cell proteomic screen on human bone marrow progenitors. This screen identifies terminal deoxynucleotidyl transferase (TdT), a specialized DNA polymerase intrinsic to VDJ recombination, broadly expressed within CD34+ progenitors prior to B/T cell emergence. While these TdT+ cells coincide with granulocyte-monocyte progenitor (GMP) immunophenotype, their accessible chromatin regions show enrichment for lymphoid-associated transcription factor (TF) motifs. TdT expression on GMPs is inversely related to the SLAM family member CD84. Prospective isolation of CD84lo GMPs demonstrates robust lymphoid potentials ex vivo, while still retaining significant myeloid differentiation capacity, akin to LMPPs. This multi-omic study identifies human bone marrow lymphoid-primed progenitors, further defining the lympho-myeloid axis in human hematopoiesis.

Multi-omic profiling reveals the endogenous and neoplastic responses to immunotherapies in cutaneous T cell lymphoma

Cutaneous T cell lymphomas (CTCLs) are skin cancers with poor survival rates and limited treatments. While immunotherapies have shown some efficacy, the immunological consequences of administering immune-activating agents to CTCL patients have not been systematically characterized. We apply a suite of high-dimensional technologies to investigate the local, cellular, and systemic responses in CTCL patients receiving either mono- or combination anti-PD-1 plus interferon-gamma (IFN-γ) therapy. Neoplastic T cells display no evidence of activation after immunotherapy. IFN-γ induces muted endogenous immunological responses, while anti-PD-1 elicits broader changes, including increased abundance of CLA+CD39+ T cells. We develop an unbiased multi-omic profiling approach enabling discovery of immune modules stratifying patients. We identify an enrichment of activated regulatory CLA+CD39+ T cells in non-responders and activated cytotoxic CLA+CD39+ T cells in leukemic patients. Our results provide insights into the effects of immunotherapy in CTCL patients and a generalizable framework for multi-omic analysis of clinical trials.

An Automation Workflow for High-Throughput Manufacturing and Analysis of Scaffold-Supported 3D Tissue Arrays

Patient-derived organoids have emerged as a useful tool to model tumour heterogeneity. Scaling these complex culture models while enabling stratified analysis of different cellular sub-populations, however, remains a challenge. One strategy to enable higher throughput organoid cultures is the scaffold-supported platform for organoid-based tissues (SPOT). SPOT allows the generation of flat, thin, and dimensionally-defined microtissues in both 96- and 384-well plate footprints that are compatible with longitudinal image-based readouts. SPOT is currently manufactured manually, however, limiting scalability. In this study, an automation approach to engineer tumour-mimetic 3D microtissues in SPOT using a liquid handler is optimized and comparable within- and between-sample variation to standard manual manufacturing is shown. Further, a liquid handler-supported cell extraction protocol to support single-cell-based end-point analysis using high-throughput flow cytometry and multiplexed cytometry by time of flight is developed. As a proof-of-value demonstration, 3D complex tissues containing different proportions of tumour and stromal cells are generated to probe the reciprocal impact of co-culture. It is also demonstrated that primary patient-derived organoids can be incorporated into the pipeline to capture patient-level tumour heterogeneity. It is envisioned that this automated 96/384-SPOT workflow will provide opportunities for future applications in high-throughput screening for novel personalized therapeutic targets.

Reagents for Mass Cytometry

Mass cytometry (cytometry by time-of-flight detection [CyTOF]) is a bioanalytical technique that enables the identification and quantification of diverse features of cellular systems with single-cell resolution. In suspension mass cytometry, cells are stained with stable heavy-atom isotope-tagged reagents, and then the cells are nebulized into an inductively coupled plasma time-of-flight mass spectrometry (ICP-TOF-MS) instrument. In imaging mass cytometry, a pulsed laser is used to ablate ca. 1 μm² spots of a tissue section. The plume is then transferred to the CyTOF, generating an image of biomarker expression. Similar measurements are possible with multiplexed ion bean imaging (MIBI). The unit mass resolution of the ICP-TOF-MS detector allows for multiparametric analysis of (in principle) up to 130 different parameters. Currently available reagents, however, allow simultaneous measurement of up to 50 biomarkers. As new reagents are developed, the scope of information that can be obtained by mass cytometry continues to increase, particularly due to the development of new small molecule reagents which enable monitoring of active biochemistry at the cellular level. This review summarizes the history and current state of mass cytometry reagent development and elaborates on areas where there is a need for new reagents. Additionally, this review provides guidelines on how new reagents should be tested and how the data should be presented to make them most meaningful to the mass cytometry user community.

INFLECT: an R-package for cytometry cluster evaluation using marker modality

Background

Current methods of high-dimensional unsupervised clustering of mass cytometry data lack means to monitor and evaluate clustering results. Whether unsupervised clustering is correct is typically evaluated by agreement with dimensionality reduction techniques or based on benchmarking with manually classified cells. The ambiguity and lack of reproducibility of sequential gating has been replaced with ambiguity in interpretation of clustering results. On the other hand, spurious overclustering of data leads to loss of statistical power. We have developed INFLECT, an R-package designed to give insight in clustering results and provide an optimal number of clusters. In our approach, a mass cytometry dataset is overclustered intentionally to ensure the smallest phenotypically different subsets are captured using FlowSOM. A range of metacluster number endpoints are generated and evaluated using marker interquartile range and distribution unimodality checks. The fraction of marker distributions that pass these checks is taken as a measure of clustering success. The fraction of unimodal distributions within metaclusters is plotted against the number of generated metaclusters and reaches a plateau of diminishing returns. The inflection point at which this occurs gives an optimal point of capturing cellular heterogeneity versus statistical power.

Results

We applied INFLECT to four publically available mass cytometry datasets of different size and number of markers. The unimodality score consistently reached a plateau, with an inflection point dependent on dataset size and number of dimensions. We tested both ConsenusClusterPlus metaclustering and hierarchical clustering. While hierarchical clustering is less computationally expensive and thus faster, it achieved similar results to ConsensusClusterPlus. The four datasets consisted of labeled data and we compared INFLECT metaclustering to published results. INFLECT identified a higher optimal number of metaclusters for all datasets. We illustrated the underlying heterogeneity within labels, showing that these labels encompass distinct types of cells.

Conclusion

INFLECT addresses a knowledge gap in high-dimensional cytometry analysis, namely assessing clustering results. This is done through monitoring marker distributions for interquartile range and unimodality across a range of metacluster numbers. The inflection point is the optimal trade-off between cellular heterogeneity and statistical power, applied in this work for FlowSOM clustering on mass cytometry datasets.

Single-cell spatial proteomic imaging for human neuropathology

Neurodegenerative disorders are characterized by phenotypic changes and hallmark proteopathies. Quantifying these in archival human brain tissues remains indispensable for validating animal models and understanding disease mechanisms. We present a framework for nanometer-scale, spatial proteomics with multiplex ion beam imaging (MIBI) for capturing neuropathological features. MIBI facilitated simultaneous, quantitative imaging of 36 proteins on archival human hippocampus from individuals spanning cognitively normal to dementia. Customized analysis strategies identified cell types and proteopathies in the hippocampus across stages of Alzheimer's disease (AD) neuropathologic change. We show microglia-pathologic tau interactions in hippocampal CA1 subfield in AD dementia. Data driven, sample independent creation of spatial proteomic regions identified persistent neurons in pathologic tau neighborhoods expressing mitochondrial protein MFN2, regardless of cognitive status, suggesting a survival advantage. Our study revealed unique insights from multiplexed imaging and data-driven approaches for neuropathologic analysis and serves broadly as a methodology for spatial proteomic analysis of archival human neuropathology. TEASER: Multiplex Ion beam Imaging enables deep spatial phenotyping of human neuropathology-associated cellular and disease features.

The Interaction of SWI/SNF with the Ribosome Regulates Translation and Confers Sensitivity to Translation Pathway Inhibitors in Cancers with Complex Perturbations

Subunits from the chromatin remodelers mammalian SWItch/Sucrose Non-Fermentable (mSWI/SNF) are mutated, deleted, or amplified in more than 40% of cancers. Understanding their functions in normal cells and the consequences of cancerous alterations will provide insight into developing new targeted therapies. Here we examined whether mSWI/SNF mutations increase cellular sensitivity to specific drugs. Taking advantage of the DepMap studies, we demonstrate that cancer cells harboring mutations of specific mSWI/SNF subunits exhibit a genetic dependency on translation factors and are sensitive to translation pathway inhibitors. Furthermore, mSWI/SNF subunits were present in the cytoplasm and interacted with the translation initiation machinery, and short-term inhibition and depletion of specific subunits decreased global translation, implicating a direct role for these factors in translation. Depletion of specific mSWI/SNF subunits also increased sensitivity to mTOR-PI3K inhibitors. In patient-derived breast cancer samples, mSWI/SNF subunits expression in both the nucleus and the cytoplasm was substantially altered. In conclusion, an unexpected cytoplasmic role for mSWI/SNF complexes in translation suggests potential new therapeutic opportunities for patients afflicted by cancers demonstrating alterations in their subunits.

SIGNIFICANCE

This work establishes direct functions for mSWI/SNF in translation and demonstrates that alterations in mSWI/SNF confer a therapeutic vulnerability to translation pathway inhibitors in cancer cells.

Supervised dimensionality reduction for exploration of single-cell data by HSS-LDA

Single-cell technologies generate large, high-dimensional datasets encompassing a diversity of omics. Dimensionality reduction captures the structure and heterogeneity of the original dataset, creating low-dimensional visualizations that contribute to the human understanding of data. Existing algorithms are typically unsupervised, using measured features to generate manifolds, disregarding known biological labels such as cell type or experimental time point. We repurpose the classification algorithm, linear discriminant analysis (LDA), for supervised dimensionality reduction of single-cell data. LDA identifies linear combinations of predictors that optimally separate a priori classes, enabling the study of specific aspects of cellular heterogeneity. We implement feature selection by hybrid subset selection (HSS) and demonstrate that this computationally efficient approach generates non-stochastic, interpretable axes amenable to diverse biological processes such as differentiation over time and cell cycle. We benchmark HSS-LDA against several popular dimensionality-reduction algorithms and illustrate its utility and versatility for the exploration of single-cell mass cytometry, transcriptomics, and chromatin accessibility data.

Memory B cell diversity: insights for optimized vaccine design

The overarching logos of mammalian memory B cells (MBCs) is to cache the potential for enhanced antibody production upon secondary exposure to cognate antigenic determinants. However, substantial phenotypic diversity has been identified across MBCs, hinting at the existence of unique origins or subfunctions within this compartment. Herein, we discuss recent advancements in human circulatory MBC subphenotyping as driven by high-throughput cell surface marker analysis and other approaches, as well as speculated and substantiated subfunctions. With this in mind, we hypothesize that the relative induction of specific circulatory MBC subsets might be used as a biomarker for optimally durable vaccines and inform vaccination strategies to subvert antigenic imprinting in the context of highly mutable pathogens such as influenza virus or SARS-CoV-2.

Human IL-10-producing B cells have diverse states that are induced from multiple B cell subsets

Regulatory B cells (Bregs) suppress immune responses through the secretion of interleukin-10 (IL-10). This immunomodulatory capacity holds therapeutic potential, yet a definitional immunophenotype for enumeration and prospective isolation of B cells capable of IL-10 production remains elusive. Here, we simultaneously quantify cytokine production and immunophenotype in human peripheral B cells across a range of stimulatory conditions and time points using mass cytometry. Our analysis shows that multiple functional B cell subsets produce IL-10 and that no phenotype uniquely identifies IL-10+ B cells. Further, a significant portion of IL-10+ B cells co-express the pro-inflammatory cytokines IL-6 and tumor necrosis factor alpha (TNFα). Despite this heterogeneity, operationally tolerant liver transplant recipients have a unique enrichment of IL-10+, but not TNFα+ or IL-6+, B cells compared with transplant recipients receiving immunosuppression. Thus, human IL-10-producing B cells constitute an induced, transient state arising from a diversity of B cell subsets that may contribute to maintenance of immune homeostasis.

Systems-based approaches to study immunometabolism

Technical advances at the interface of biology and computation, such as single-cell RNA-sequencing (scRNA-seq), reveal new layers of complexity in cellular systems. An emerging area of investigation using the systems biology approach is the study of the metabolism of immune cells. The diverse spectra of immune cell phenotypes, sparsity of immune cell numbers in vivo, limitations in the number of metabolites identified, dynamic nature of cellular metabolism and metabolic fluxes, tissue specificity, and high dependence on the local milieu make investigations in immunometabolism challenging, especially at the single-cell level. In this review, we define the systemic nature of immunometabolism, summarize cell- and system-based approaches, and introduce mathematical modeling approaches for systems interrogation of metabolic changes in immune cells. We close the review by discussing the applications and shortcomings of metabolic modeling techniques. With systems-oriented studies of metabolism expected to become a mainstay of immunological research, an understanding of current approaches toward systems immunometabolism will help investigators make the best use of current resources and push the boundaries of the discipline.

Revealing new biology from multiplexed, metal-isotope-tagged, single-cell readouts

Mass cytometry (MC) is a recent technology that pairs plasma-based ionization of cells in suspension with time-of-flight (TOF) mass spectrometry to sensitively quantify the single-cell abundance of metal-isotope-tagged affinity reagents to key proteins, RNA, and peptides. Given the ability to multiplex readouts (~50 per cell) and capture millions of cells per experiment, MC offers a robust way to assay rare, transitional cell states that are pertinent to human development and disease. Here, we review MC approaches that let us probe the dynamics of cellular regulation across multiple conditions and sample types in a single experiment. Additionally, we discuss current limitations and future extensions of MC as well as computational tools commonly used to extract biological insight from single-cell proteomic datasets.

A guide to interrogating immunometabolism

The metabolic charts memorized in early biochemistry courses, and then later forgotten, have come back to haunt many immunologists with new recognition of the importance of these pathways. Metabolites and the activity of metabolic pathways drive energy production, macromolecule synthesis, intracellular signalling, post-translational modifications and cell survival. Immunologists who identify a metabolic phenotype in their system are often left wondering where to begin and what does it mean? Here, we provide a framework for navigating and selecting the appropriate biochemical techniques to explore immunometabolism. We offer recommendations for initial approaches to develop and test metabolic hypotheses and how to avoid common mistakes. We then discuss how to take things to the next level with metabolomic approaches, such as isotope tracing and genetic approaches. By proposing strategies and evaluating the strengths and weaknesses of different methodologies, we aim to provide insight, note important considerations and discuss ways to avoid common misconceptions. Furthermore, we highlight recent studies demonstrating the power of these metabolic approaches to uncover the role of metabolism in immunology. By following the framework in this Review, neophytes and seasoned investigators alike can venture into the emerging realm of cellular metabolism and immunity with confidence and rigour.

Regulatory and IgE+ B Cells in Allergic Asthma

Allergic asthma is triggered by inhalation of environmental allergens resulting in bronchial constriction and inflammation, which leads to clinical symptoms such as wheezing, coughing, and difficulty breathing. Asthmatic airway inflammation is initiated by inflammatory mediators released by granulocytic cells. However, the immunoglobulin E (IgE) antibody is necessary for the initiation of the allergic cascade, and IgE is produced and released exclusively by memory B cells and plasma cells. Acute allergen exposure has also been shown to increase IgE levels in the airways of patients diagnosed with allergic asthma; however, more studies are needed to understand local airway inflammation. Additionally, regulatory B cells (B_regs) have been shown to modulate IgE-mediated inflammatory processes in allergic asthma pathogenesis, particularly in mouse models of allergic airway disease. However, the levels and function of these IgE⁺ B cells and B_regs remain to be elucidated in human models of asthma. The overall objective for this chapter is to provide detailed methodological, and insightful technological advances to study the biology of B cells in allergic asthma pathogenesis. Specifically, we will describe how to investigate the frequency and function of IgE⁺ B cells and B_regs in allergic asthma, and the kinetics of these cells after allergen exposure in a human asthma model.

An Integrated Multi-omic Single-Cell Atlas of Human B Cell Identity

B cells are capable of a wide range of effector functions including antibody secretion, antigen presentation, cytokine production, and generation of immunological memory. A consistent strategy for classifying human B cells by using surface molecules is essential to harness this functional diversity for clinical translation. We developed a highly multiplexed screen to quantify the co-expression of 351 surface molecules on millions of human B cells. We identified differentially expressed molecules and aligned their variance with isotype usage, VDJ sequence, metabolic profile, biosynthesis activity, and signaling response. Based on these analyses, we propose a classification scheme to segregate B cells from four lymphoid tissues into twelve unique subsets, including a CD45RB⁺CD27⁻ early memory population, a class-switched CD39⁺ tonsil-resident population, and a CD19^hiCD11c⁺ memory population that potently responds to immune activation. This classification framework and underlying datasets provide a resource for further investigations of human B cell identity and function.

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A mass cytometry screen reveals 98 surface molecules expressed by human B cells

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High-dimensional analysis identifies twelve B cell subsets across four tissues

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CD45RB, CD11c, CD39, CD73, and CD95 define subsets of antigen-experienced B cells

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Isotype usage, signaling, and metabolism vary in accordance with cell surface phenotype

Immunometabolism in the Single-Cell Era

Emerging research has identified metabolic pathways that are crucial for the proper regulation of immune cells and how, when deranged, they can cause immune dysfunction and disease progression. However, due to technical limitations such insights have relied heavily on bulk measurements in immune cells, often activated in vitro. But with the emergence of single-cell applications, researchers can now estimate the metabolic state of individual immune cells in clinical samples. Here, we review these single-cell techniques and their ability to validate common principles in immunometabolism, while also revealing context-dependent metabolic heterogeneity within the immune cell compartment. We also discuss current gaps and limitations, as well as identify future opportunities to move the field forward toward the development of therapeutic targets and improved diagnostic capabilities.

Immune monitoring using mass cytometry and related high-dimensional imaging approaches

The cellular complexity and functional diversity of the human immune system necessitate the use of high-dimensional single-cell tools to uncover its role in multifaceted diseases such as rheumatic diseases, as well as other autoimmune and inflammatory disorders. Proteomic technologies that use elemental (heavy metal) reporter ions, such as mass cytometry (also known as CyTOF) and analogous high-dimensional imaging approaches (including multiplexed ion beam imaging (MIBI) and imaging mass cytometry (IMC)), have been developed from their low-dimensional counterparts, flow cytometry and immunohistochemistry, to meet this need. A growing number of studies have been published that use these technologies to identify functional biomarkers and therapeutic targets in rheumatic diseases, but the full potential of their application to rheumatic disease research has yet to be fulfilled. This Review introduces the underlying technologies for high-dimensional immune monitoring and discusses aspects necessary for their successful implementation, including study design principles, analytical tools and future developments for the field of rheumatology.

Multimodal single-cell approaches shed light on T cell heterogeneity

Single-cell methods have revolutionized the study of T cell biology by enabling the identification and characterization of individual cells. This has led to a deeper understanding of T cell heterogeneity by generating functionally relevant measurements - like gene expression, surface markers, chromatin accessibility, T cell receptor sequences - in individual cells. While these methods are independently valuable, they can be augmented when applied jointly, either on separate cells from the same sample or on the same cells. Multimodal approaches are already being deployed to characterize T cells in diverse disease contexts and demonstrate the value of having multiple insights into a cell's function. But, these data sets pose new statistical challenges for integration and joint analysis.

TRAIL-induced variation of cell signaling states provides nonheritable resistance to apoptosis

TNFα-related apoptosis-inducing ligand (TRAIL), specifically initiates programmed cell death, but often fails to eradicate all cells, making it an ineffective therapy for cancer. This fractional killing is linked to cellular variation that bulk assays cannot capture. Here, we quantify the diversity in cellular signaling responses to TRAIL, linking it to apoptotic frequency across numerous cell systems with single-cell mass cytometry (CyTOF). Although all cells respond to TRAIL, a variable fraction persists without apoptotic progression. This cell-specific behavior is nonheritable where both the TRAIL-induced signaling responses and frequency of apoptotic resistance remain unaffected by prior exposure. The diversity of signaling states upon exposure is correlated to TRAIL resistance. Concomitantly, constricting the variation in signaling response with kinase inhibitors proportionally decreases TRAIL resistance. Simultaneously, TRAIL-induced de novo translation in resistant cells, when blocked by cycloheximide, abrogated all TRAIL resistance. This work highlights how cell signaling diversity, and subsequent translation response, relates to nonheritable fractional escape from TRAIL-induced apoptosis. This refined view of TRAIL resistance provides new avenues to study death ligands in general.

Minimizing Batch Effects in Mass Cytometry Data

Cytometry by Time-Of-Flight (CyTOF) uses antibodies conjugated to isotopically pure metals to identify and quantify a large number of cellular features with single-cell resolution. A barcoding approach allows for 20 unique samples to be pooled and processed together in one tube, reducing the intra-barcode technical variability. However, with only 20 samples per barcode, multiple barcode sets (batches) are required to address questions in robustly powered study designs. A batch adjustment procedure is required to reduce variability across batches and to facilitate direct comparison of runs performed across multiple barcodes run over weeks, months, or years. We describe a method using technical replicates that are included in each run to determine and apply an appropriate adjustment per batch without manual intervention. The use of technical replicate samples (i.e., anchors or reference samples) avoids assumptions of sample homogeneity among batches, and allows direct estimation of batch effects and appropriate adjustment parameters applicable to all samples within a batch. Quantification of cell subpopulations and mean signal intensity pre- and post-adjustment using both manual gating and unsupervised clustering demonstrate substantial mitigation of batch effects in the anchor samples used for this adjustment calculation, and in a second validation set of technical replicates.

Physiological roles of antisense RNAs in prokaryotes

Prokaryotes encounter constant and often brutal modifications to their environment. In order to survive, they need to maintain fitness, which includes adapting their protein expression patterns. Many factors control gene expression but this review focuses on just one, namely antisense RNAs (asRNAs), a class of non-coding RNAs (ncRNAs) characterized by their location in cis and their perfect complementarity with their targets. asRNAs were considered for a long time to be trivial and only to be found on mobile genetic elements. However, recent advances in methodology have revealed that their abundance and potential activities have been underestimated. This review aims to illustrate the role of asRNA in various physiologically crucial functions in both archaea and bacteria, which can be regrouped in three categories: cell maintenance, horizontal gene transfer and virulence. A literature survey of asRNAs demonstrates the difficulties to characterize and assign a role to asRNAs. With the aim of facilitating this task, we describe recent technological advances that could be of interest to identify new asRNAs and to discover their function.