CytoGLMM: conditional differential analysis for flow and mass cytometry experiments

Mass cytometric analysis of circulating monocyte subsets in a murine model of diabetic gastroparesis

Circulating monocytes (Mo) are precursors to a subset of gastric resident muscularis macrophages. Changes in muscularis macrophages (MMs) result in delayed gastric emptying (DGE) in diabetic gastroparesis. However, the dynamics of Mo in the development of DGE in an animal model are unknown. Using cytometry by time-of-flight and computational approaches, we show a high heterogeneity within the Mo population. In DGE mice, via unbiased clustering, we identified two reduced Mo clusters that exhibit migratory phenotype (Ly6ChiCCR2hi-intCD62LhiLy6GhiCD45RhiMERTKhiintLGALS3intCD14intCX3CR1lowSiglec-Hint-low) resembling classical Mo (CMo-like). All markers enriched in these clusters are known to regulate cell differentiation, proliferation, adhesion, and migration. Trajectory inference analysis predicted these Mo as precursors to subsequent Mo lineages. In gastric muscle tissue, we demonstrated an increase in the gene expression levels of chemokine receptor C-C chemokine receptor type 2 (Ccr2) and its C-C motif ligand 2 (Ccl2), suggesting increased trafficking of classical-Mo. These findings establish a link between two CMo-like clusters and the development of the DGE phenotype and contribute to a better understanding of the heterogenicity of the Mo population.NEW & NOTEWORTHY Using 32 immune cell surface markers, we identified 23 monocyte clusters in murine blood. Diabetic gastroparesis was associated with a significant decrease in two circulating classical monocyte-like clusters and an upregulation of the Ccr2-Ccl2 axis in the gastric muscularis propria, suggesting increased tissue monocyte migration. This study offers new targets by pointing to a possible role for two classical monocyte subsets connected to the Ccr2-Ccl2 axis.

Visual Quality Control With CytoMDS , a Bioconductor Package for Low Dimensional Representation of Cytometry Sample Distances

Quality Control (QC) of samples is an essential preliminary step in cytometry data analysis. Notably, the identification of potential batch effects and outlying samples is paramount to avoid mistaking these effects for true biological signals in downstream analyses. However, this task can prove to be delicate and tedious, especially for datasets with dozens of samples. Here, we present CytoMDS, a Bioconductor package implementing a dedicated method for low-dimensional representation of cytometry samples composed of marker expressions for up to millions of single cells. This method allows a global representation of all samples of a study, with one single point per sample, in such a way that projected distances can be visually interpreted. CytoMDS uses Earth Mover's Distance for assessing dissimilarities between multi-dimensional distributions of marker expression and Multi-Dimensional Scaling for low-dimensional projection of distances. Some additional visualization tools, both for projection quality diagnosis and for user interpretation of the projection coordinates, are also provided in the package. We demonstrate the strengths and advantages of CytoMDS for QC of cytometry data on three real biological datasets, revealing the presence of low-quality samples, batch effects, and biological signal between sample groups.

Distinct immune profiles in children living with HIV based on timing and duration of suppressive antiretroviral treatment

Timely initiation of antiretroviral therapy (ART) remains a major challenge in the effort to treat children living with HIV ("CLH") and little is known regarding the dynamics of immune normalization following ART in CLH with varying times to and durations of ART. Here, we leveraged two cohorts of virally-suppressed CLH from Nairobi, Kenya to examine differences in the peripheral immune systems between two cohorts of age-matched children (to control for immune changes with age): one group which initiated ART during early HIV infection and had been on ART for 5-6 years at evaluation (early, long-term treated; "ELT" cohort), and one group which initiated ART later and had been on ART for approximately 9 months at evaluation (delayed, short-term treated; "DST" cohort). We profiled PBMC and purified NK cells from these two cohorts by mass cytometry time-of-flight (CyTOF). Although both groups of CLH had undetectable viral RNA load at evaluation, there were marked differences in both immune composition and immune phenotype between the ELT cohort and the DST cohort. DST donors had reduced CD4 T cell percentages, decreased naive to effector memory T cell ratios, and markedly higher expression of stress-induced markers. Conversely, ELT donors had higher naive to effector memory T cell ratios, low expression of stress-induced markers, and increased expression of markers associated with an effective antiviral response and resolution of inflammation. Collectively, our results demonstrate key differences in the immune systems of virally-suppressed CLH with different ages at ART initiation and durations of treatment and provide further rationale for emphasizing early onset of ART.

Comprehensive evaluation and practical guideline of gating methods for high-dimensional cytometry data: manual gating, unsupervised clustering, and auto-gating

Cytometry is an advanced technique for simultaneously identifying and quantifying many cell surface and intracellular proteins at a single-cell resolution. Analyzing high-dimensional cytometry data involves identifying and quantifying cell populations based on their marker expressions. This study provided a quantitative review and comparison of various ways to phenotype cellular populations within the cytometry data, including manual gating, unsupervised clustering, and supervised auto-gating. Six datasets from diverse species and sample types were included in the study, and manual gating with two hierarchical layers was used as the truth for evaluation. For manual gating, results from five researchers were compared to illustrate the gating consistency among different raters. For unsupervised clustering, 23 tools were quantitatively compared in terms of accuracy with the truth and computing cost. While no method outperformed all others, several tools, including PAC-MAN, CCAST, FlowSOM, flowClust, and DEPECHE, generally demonstrated strong performance. For supervised auto-gating methods, four algorithms were evaluated, where DeepCyTOF and CyTOF Linear Classifier performed the best. We further provided practical recommendations on prioritizing gating methods based on different application scenarios. This study offers comprehensive insights for biologists to understand diverse gating methods and choose the best-suited ones for their applications.

RUCova: Removal of Unwanted Covariance in mass cytometry data

MOTIVATION

High dimensional single-cell mass cytometry data are confounded by unwanted covariance due to variations in cell size and staining efficiency, making analysis, and interpretation challenging.

RESULTS

We present RUCova, a novel method designed to address confounding factors in mass cytometry data. RUCova removes unwanted covariance from measured markers applying multivariate linear regression based on surrogates of sources of unwanted covariance (SUCs) and principal component analysis (PCA). We exemplify the use of RUCova and show that it effectively removes unwanted covariance while preserving genuine biological signals. Our results demonstrate the efficacy of RUCova in elucidating complex data patterns, facilitating the identification of activated signalling pathways, and improving the classification of important cell populations such as apoptotic cells. By providing a robust framework for data normalization and interpretation, RUCova enhances the accuracy and reliability of mass cytometry analyses, contributing to advances in our understanding of cellular biology and disease mechanisms.

AVAILABILITY AND IMPLEMENTATION

The R package is available on https://github.com/molsysbio/RUCova. Detailed documentation, data, and the code required to reproduce the results are available on https://doi.org/10.5281/zenodo.10913464.

Autophagy counters inflammation-driven glycolytic impairment in aging hematopoietic stem cells

Autophagy is central to the benefits of longevity signaling programs and to hematopoietic stem cell (HSC) response to nutrient stress. With age, a subset of HSCs increases autophagy flux and preserves regenerative capacity, but the signals triggering autophagy and maintaining the functionality of autophagy-activated old HSCs (oHSCs) remain unknown. Here, we demonstrate that autophagy is an adaptive cytoprotective response to chronic inflammation in the aging murine bone marrow (BM) niche. We find that inflammation impairs glucose uptake and suppresses glycolysis in oHSCs through Socs3-mediated inhibition of AKT/FoxO-dependent signaling, with inflammation-mediated autophagy engagement preserving functional quiescence by enabling metabolic adaptation to glycolytic impairment. Moreover, we show that transient autophagy induction via a short-term fasting/refeeding paradigm normalizes glycolytic flux and significantly boosts oHSC regenerative potential. Our results identify inflammation-driven glucose hypometabolism as a key driver of HSC dysfunction with age and establish autophagy as a targetable node to reset oHSC regenerative capacity.

NKp30 and NKG2D contribute to natural killer recognition of HIV-infected cells

Natural killer (NK) cells respond rapidly in early HIV-1 infection. HIV-1 prevention and control strategies harnessing NK cells could be enabled by mechanistic understanding of how NK cells recognize HIV-infected T cells. Here, we profiled the phenotype of human primary NK cells responsive to autologous HIV-1-infected CD4 + T cells in vitro. We characterized the patterns of NK cell ligand expression on CD4 + T cells at baseline and after infection with a panel of transmitted/founder HIV-1 strains to identify key receptor-ligand pairings. CRISPR editing of CD4 + T cells to knockout the NKp30 ligand B7-H6, or the NKG2D ligands MICB or ULBP2 reduced NK cell responses to HIV-infected cells in some donors. In contrast, overexpression of NKp30 or NKG2D in NK cells enhanced their targeting of HIV-infected cells. Collectively, we identified receptor-ligand pairs including NKp30:B7-H6 and NKG2D:MICB/ULBP2 that contribute to NK cell recognition of HIV-infected cells.

spillR: spillover compensation in mass cytometry data

MOTIVATION

Channel interference in mass cytometry can cause spillover and may result in miscounting of protein markers. Chevrier et al. introduce an experimental and computational procedure to estimate and compensate for spillover implemented in their R package CATALYST. They assume spillover can be described by a spillover matrix that encodes the ratio between the signal in the unstained spillover receiving and stained spillover emitting channel. They estimate the spillover matrix from experiments with beads. We propose to skip the matrix estimation step and work directly with the full bead distributions. We develop a nonparametric finite mixture model and use the mixture components to estimate the probability of spillover. Spillover correction is often a pre-processing step followed by downstream analyses, and choosing a flexible model reduces the chance of introducing biases that can propagate downstream.

RESULTS

We implement our method in an R package spillR using expectation-maximization to fit the mixture model. We test our method on simulated, semi-simulated, and real data from CATALYST. We find that our method compensates low counts accurately, does not introduce negative counts, avoids overcompensating high counts, and preserves correlations between markers that may be biologically meaningful.

AVAILABILITY AND IMPLEMENTATION

Our new R package spillR is on bioconductor at bioconductor.org/packages/spillR. All experiments and plots can be reproduced by compiling the R markdown file spillR_paper.Rmd at github.com/ChristofSeiler/spillR_paper.

Cytomulate: accurate and efficient simulation of CyTOF data

Recently, many analysis tools have been devised to offer insights into data generated via cytometry by time-of-flight (CyTOF). However, objective evaluations of these methods remain absent as most evaluations are conducted against real data where the ground truth is generally unknown. In this paper, we develop Cytomulate, a reproducible and accurate simulation algorithm of CyTOF data, which could serve as a foundation for future method development and evaluation. We demonstrate that Cytomulate can capture various characteristics of CyTOF data and is superior in learning overall data distributions than single-cell RNA-seq-oriented methods such as scDesign2, Splatter, and generative models like LAMBDA.

High-dimensional profiling of pediatric immune responses to solid organ transplantation

Solid organ transplant remains a life-saving therapy for children with end-stage heart, lung, liver, or kidney disease; however, ∼33% of allograft recipients experience acute rejection within the first year after transplant. Our ability to detect early rejection is hampered by an incomplete understanding of the immune changes associated with allograft health, particularly in the pediatric population. We performed detailed, multilineage, single-cell analysis of the peripheral blood immune composition in pediatric solid organ transplant recipients, with high-dimensional mass cytometry. Supervised and unsupervised analysis methods to study cell-type proportions indicate that the allograft type strongly influences the post-transplant immune profile. Further, when organ-specific differences are considered, graft health is associated with changes in the proportion of distinct T cell subpopulations. Together, these data form the basis for mechanistic studies into the pathobiology of rejection and allow for the development of new immunosuppressive agents with greater specificity.

Computational Methods for Single-Cell Proteomics

Advances in single-cell proteomics technologies have resulted in high-dimensional datasets comprising millions of cells that are capable of answering key questions about biology and disease. The advent of these technologies has prompted the development of computational tools to process and visualize the complex data. In this review, we outline the steps of single-cell and spatial proteomics analysis pipelines. In addition to describing available methods, we highlight benchmarking studies that have identified advantages and pitfalls of the currently available computational toolkits. As these technologies continue to advance, robust analysis tools should be developed in tandem to take full advantage of the potential biological insights provided by these data.

Advances in HIV Research Using Mass Cytometry

PURPOSE OF REVIEW

This review describes how advances in CyTOF and high-dimensional analysis methods have furthered our understanding of HIV transmission, pathogenesis, persistence, and immunity.

RECENT FINDINGS

CyTOF has generated important insight on several aspects of HIV biology: (1) the differences between cells permissive to productive vs. latent HIV infection, and the HIV-induced remodeling of infected cells; (2) factors that contribute to the persistence of the long-term HIV reservoir, in both blood and tissues; and (3) the impact of HIV on the immune system, in the context of both uncontrolled and controlled infection. CyTOF and high-dimensional analysis tools have enabled in-depth assessment of specific host antigens remodeled by HIV, and have revealed insights into the features of HIV-infected cells enabling them to survive and persist, and of the immune cells that can respond to and potentially control HIV replication. CyTOF and other related high-dimensional phenotyping approaches remain powerful tools for translational research, and applied HIV to cohort studies can inform on mechanisms of HIV pathogenesis and persistence, and potentially identify biomarkers for viral eradication or control.

Protocol for identification and computational analysis of human natural killer cells using flow cytometry and R

Identifying differential protein expression is routinely used to delineate natural killer (NK) cells from various sample cohorts. This protocol describes key steps for NK cell analysis: identifying human NK cells using flow gating, data export from FlowJo, data loading in R, dimensionality reduction and visualization with Uniform Manifold Approximation and Projection, and generalized linear modeling with CyotGLMM. These analyses can help generate potential biomarkers of interest to identify NK cells across aging, treatment groups, and others. For complete details on the use and execution of this protocol, please refer to Kroll et al. (2022).1.

Mucosal-homing natural killer cells are associated with aging in persons living with HIV

Natural killer (NK) cells are critical modulators of HIV transmission and disease. Recent evidence suggests a loss of NK cell cytotoxicity during aging, yet analysis of NK cell biology and aging in people with HIV (PWH) is lacking. Herein, we perform comprehensive analyses of people aging with and without HIV to determine age-related NK phenotypic changes. Utilizing high-dimensional flow cytometry, we analyze 30 immune-related proteins on peripheral NK cells from healthy donors, PWH with viral suppression, and viremic PWH. NK cell phenotypes are dynamic across aging but change significantly in HIV and on antiretroviral drug therapy (ART). NK cells in healthy aging show increasing ⍺4β7 and decreasing CCR7 expression and a reverse phenomenon in PWH. These HIV-associated trafficking patterns could be due to NK cell recruitment to HIV reservoir formation in lymphoid tissue or failed mucosal signaling in the HIV-infected gut but appear to be tight delineators of age-related NK cell changes.

Challenges in translational machine learning

Machine learning (ML) algorithms are increasingly being used to help implement clinical decision support systems. In this new field, we define as "translational machine learning", joint efforts and strong communication between data scientists and clinicians help to span the gap between ML and its adoption in the clinic. These collaborations also improve interpretability and trust in translational ML methods and ultimately aim to result in generalizable and reproducible models. To help clinicians and bioinformaticians refine their translational ML pipelines, we review the steps from model building to the use of ML in the clinic. We discuss experimental setup, computational analysis, interpretability and reproducibility, and emphasize the challenges involved. We highly advise collaboration and data sharing between consortia and institutes to build multi-centric cohorts that facilitate ML methodologies that generalize across centers. In the end, we hope that this review provides a way to streamline translational ML and helps to tackle the challenges that come with it.

Application of Machine Learning for Cytometry Data

Modern cytometry technologies present opportunities to profile the immune system at a single-cell resolution with more than 50 protein markers, and have been widely used in both research and clinical settings. The number of publicly available cytometry datasets is growing. However, the analysis of cytometry data remains a bottleneck due to its high dimensionality, large cell numbers, and heterogeneity between datasets. Machine learning techniques are well suited to analyze complex cytometry data and have been used in multiple facets of cytometry data analysis, including dimensionality reduction, cell population identification, and sample classification. Here, we review the existing machine learning applications for analyzing cytometry data and highlight the importance of publicly available cytometry data that enable researchers to develop and validate machine learning methods.

Stereotypic Expansion of T Regulatory and Th17 Cells during Infancy Is Disrupted by HIV Exposure and Gut Epithelial Damage

Few studies have investigated immune cell ontogeny throughout the neonatal and early pediatric period, when there is often increased vulnerability to infections. In this study, we evaluated the dynamics of two critical T cell populations, T regulatory (Treg) cells and Th17 cells, over the first 36 wk of human life. First, we observed distinct CD4+ T cells phenotypes between cord blood and peripheral blood, collected within 12 h of birth, showing that cord blood is not a surrogate for newborn blood. Second, both Treg and Th17 cells expanded in a synchronous fashion over 36 wk of life. However, comparing infants exposed to HIV in utero, but remaining uninfected, with HIV-unexposed uninfected control infants, there was a lower frequency of peripheral blood Treg cells at birth, resulting in a delayed expansion, and then declining again at 36 wk. Focusing on birth events, we found that Treg cells coexpressing CCR4 and α4β7 inversely correlated with plasma concentrations of CCL17 (the ligand for CCR4) and intestinal fatty acid binding protein, IL-7, and CCL20. This was in contrast with Th17 cells, which showed a positive association with these plasma analytes. Thus, despite the stereotypic expansion of both cell subsets over the first few months of life, there was a disruption in the balance of Th17 to Treg cells at birth likely being a result of gut damage and homing of newborn Treg cells from the blood circulation to the gut.

A systematic comparison of novel and existing differential analysis methods for CyTOF data

Cytometry techniques are widely used to discover cellular characteristics at single-cell resolution. Many data analysis methods for cytometry data focus solely on identifying subpopulations via clustering and testing for differential cell abundance. For differential expression analysis of markers between conditions, only few tools exist. These tools either reduce the data distribution to medians, discarding valuable information, or have underlying assumptions that may not hold for all expression patterns. Here, we systematically evaluated existing and novel approaches for differential expression analysis on real and simulated CyTOF data. We found that methods using median marker expressions compute fast and reliable results when the data are not strongly zero-inflated. Methods using all data detect changes in strongly zero-inflated markers, but partially suffer from overprediction or cannot handle big datasets. We present a new method, CyEMD, based on calculating the earth mover's distance between expression distributions that can handle strong zero-inflation without being too sensitive. Additionally, we developed CYANUS - CYtometry ANalysis Using Shiny - a user-friendly R Shiny App allowing the user to analyze cytometry data with state-of-the-art tools, including well-performing methods from our comparison. A public web interface is available at https://exbio.wzw.tum.de/cyanus/.