Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum

Single-Cell/Particle Sample Introduction Device for Mass Cytometry Based on Gas-Driven Flow Focusing

Mass cytometry is considered the second generation of flow cytometry technology, which uses metal-labeled antibodies instead of traditional fluorescent antibodies and can measure much more markers from a single cell simultaneously. However, due to frequent clogging of the capillary and nebulizer, it is still a complicated task to perform the sample introduction smoothly for a long-term determination. Herein, a plug-and-play sample introduction device for mass cytometry analysis was developed based on a gas-driven flow focusing protocol. The device is integrated with a virtual hole to generate liquid jet and omit a conventional nebulizer. Single-cell monodispersion can be achieved by liquid jet breakup instead of the commonly used nebulization by the Venturi effect, thus avoiding clogging of thin central capillary. The device has high universality and compatibility and can be plug-and-play on a mass cytometry instrument or common inductively coupled plasma mass spectrometry for single-cell or single-particle determination. High-speed microscopy imaging was used to capture and study the dynamic processes of monodispersion on microsphere suspensions achieved by the device. We also compared the analysis capabilities when using the device and a conventional nebulizer in mass cytometry analysis of four element calibration beads and cell samples. The results obtained by using the flow focusing device and the conventional nebulizer show great consistency, which means the device has no negative impact on the detection performance of mass cytometry and single-cell/particle monodispersion can be achieved without clogging.

Characterization of immune phenotypes in peripheral blood of adult renal transplant recipients using mass cytometry (CyTOF)

Chronic immunosuppressive therapies are crucial in organ transplantation but can increase the risk of opportunistic infections and cancer over time. We investigated immune status changes in 10 kidney transplant patients and 11 age-matched healthy adults using broad in vitro stimulation of subject-derived peripheral blood mononuclear cells followed by mass cytometry by time of flight over 6 mo. Overall, the immune cells of transplant patients exhibited increased CD8+ T cell activation and differentiation compared with healthy donors, with elevated CD8+ CD57+, MIP-1β, and interferon γ production (P < 0.05, P < 0.05, and P < 0.01, respectively). CD107a and granzyme B expression were increased in CD8+ T cells and CD56bright natural killer cells (P < 0.05 and P < 0.01, respectively), while T regulatory cells had decreased interleukin-10 production (P < 0.05). These changes indicated a proinflammatory environment influenced by induction therapy and ongoing maintenance drugs. Additionally, transplant recipients displayed signs of immune modulation, including decreased tumor necrosis factor α, interferon γ, and MIP-1β expression in γδT cells (P < 0.05 and P < 0.01), and reduced interleukin-17 and granulocyte-macrophage colony-stimulating factor expression in CD8+ T memory cell subsets (P < 0.05). The diverse functional changes underscore the importance of comprehensive immune status profiling for optimizing individual treatment strategies and developing better immunosuppressants that specifically target activated cell populations.

Predicting cell properties with AI from 3D imaging flow cytometer data

Predicting the properties of tissues or organisms from the genomics data is widely accepted by the medical community. Here we ask a question: can we predict the properties of each individual cell? Single-cell genomics does not work because the RNA sequencing process destroys the cell, not allowing us to verify our predictions. To test the hypothesis, we investigate the approach of using AI to analyze single-cell images obtained from a 3D imaging flow cytometer. We analyze the cell image at day zero and make the AI-assisted cell property prediction. The prediction is then examined later when the cells continue to live and develop. Our preliminary results are promising, showing 88% accuracy in predicting cells that will have a high protein expression level. The technique can have strong ramifications and impact on preventive medicine, drug development, cell therapy, and fundamental biomedical research.

Elevated frequencies of activated memory B cells in multiple sclerosis are reset to healthy control levels after B cell depletion with Ocrelizumab

In multiple sclerosis (MS) the B cell depleting drug ocrelizumab has shown high efficacy in reducing inflammatory activity. Its mechanism of action is unclear due to B cell subset complexity and unknown roles in pathogenesis. Here, we comprehensively phenotyped and quantitated peripheral blood B cell subsets before and after ocrelizumab infusion to gain insight into the fate of B cell subsets with pathogenic potential. Peripheral blood B cells were collected from treatment naïve patients at baseline and months one, three, and six following the first course of ocrelizumab treatment; at 6 months following the second treatment cycle; ∼14 months following their last infusion; and from healthy controls. Flow cytometry combined with cluster analysis was used to track depletion and repletion of naïve, memory, and antibody secreting cells. By month one, naïve B cells were depleted, but a small subset of memory B cells were retained with no depletion of antibody secreting cells. Uniform manifold approximation and projection for dimension reduction analysis of flow cytometry data revealed two non-class switched naïve clusters and two class switched memory clusters. One class switched cluster was activated in MS patients but largely absent in healthy controls. Both memory B cell subsets underwent depletion after a single six-month course of ocrelizumab treatment after which their proportions were reset to heathy control levels. These observations suggest that activated class-switched memory B cells could serve as a biomarker of recent or ongoing MS disease activity to guide redosing.

Breaking Barcode Limits: Metal Nanoparticle Lego Brick Self-Assembly for High-Throughput Screening

As precision medicine increasingly reveals the biological diversity among individuals, the demand for higher-throughput screening techniques, particularly suspension array technologies capable of more multiplexing from smaller samples in a single run, is intensifying. However, advancements in the multiplexing capability of current suspension platforms have lagged with limited alleviation, necessitating breakthroughs for innovative solutions that enable larger-scale measurements. Here, we introduce such a breakthrough with a novel mass-cytometric barcode engineering by metal nanoparticle-based "Lego Brick"-like self-assembly for high-throughput barcode design and capacity amplification. The suspension array capacity can be expanded to over 20,500 unique barcodes by flexibly assembling just 10 types of barcoding units (metal nanoparticles) onto the surface of the barcoding center (magnetic spheres) through a universal biotin-streptavidin binding template, significantly enhancing both throughput and versatility. Further multiplexed immunoassay, termed MassMAP, demonstrates high-throughput profiling of cancer biomarkers, highlighting the revolutionary potential of Lego Brick self-assembly in massive cytometric screening for higher-throughput applications.

The Effect of Cryopreservation on T-Cell Subsets by Flow Cytometry Automated Algorithmic Analysis and Conventional Analysis

BACKGROUND

Low-temperature cryopreservation is a common method for scientific research and clinical sample preservation when utilizing flow cytometry. In flow cytometry data analysis, traditional manual "gating" is susceptible to past experience and faces the challenge of manual subjective bias, time-consuming, and multidimensional data analysis. With the development of algorithms, the advantages of dimensionality reduction and clustering in result analysis are gradually becoming more prominent.

METHODS

Flow cytometry was used to detect the effects of cryopreservation and freeze-thaw cycle on T-cell subsets, and to analyze the data using automated algorithmic analysis and conventional manual "gating" methods.

RESULTS

The results showed that the number and viability of cells decreased slightly after one freeze-thaw within 2 weeks of cryopreservation, and there was no significant change in the subpopulation proportions and spatial locations by both analysis methods. The changes were significant with the increase of cryopreservation time and freeze-thaw cycle, which may be due to changes in the molecular conformation of the maker as a result of cryopreservation.

CONCLUSION

The results indicate that both analysis methods have reached similar conclusions, but the repeatability and objectivity of automated algorithmic analysis have compensated for the uncertainty brought about by the subjective discretization of traditional manual "gating." In addition, the automated algorithmic analysis more intuitively highlights the spatial positional variations in the relationships between cell populations.

Uridine Metabolism as a Targetable Metabolic Achilles' Heel for chemo-resistant B-ALL

Relapse continues to limit survival for patients with B-cell acute lymphoblastic leukemia (B-ALL). Previous studies have independently implicated activation of B-cell developmental signaling pathways and increased glucose consumption with chemo-resistance and relapse risk. Here, we connect these observations, demonstrating that B-ALL cells with active signaling, defined by high expression of phosphorylated ribosomal protein S6 ("pS6+ cells"), are metabolically unique and glucose dependent. Isotope tracing and metabolic flux analysis confirm that pS6+ cells are highly glycolytic and notably sensitive to glucose deprivation, relying on glucose for de novo nucleotide synthesis. Uridine, but not purine or pyrimidine, rescues pS6+ cells from glucose deprivation, highlighting uridine is essential for their survival. Active signaling in pS6+ cells drives uridine production through activating phosphorylation of carbamoyl phosphate synthetase (CAD), the enzyme catalyzing the initial steps of uridine synthesis. Inhibition of signaling abolishes glucose dependency and CAD phosphorylation in pS6+ cells. Primary pS6+ cells demonstrate high expression of uridine synthesis proteins, including dihydroorotate dehydrogenase (DHODH), the rate-limiting catalyst of de novo uridine synthesis. Gene expression demonstrates that increased expression of DHODH is associated with relapse and inferior event-free survival after chemotherapy. Further, the majority of B-ALL genomic subtypes demonstrate activity of DHODH. Inhibiting DHODH using BAY2402232 effectively kills pS6+ cells in vitro, with its IC50 correlated with the strength of pS6 signaling across 14 B-ALL cell lines and patient-derived xenografts (PDX). In vivo DHODH inhibition prolongs survival and decreases leukemia burden in pS6+ B-ALL cell line and PDX models. These findings link active signaling to uridine dependency in B-ALL cells and an associated risk of relapse. Targeting uridine synthesis through DHODH inhibition offers a promising therapeutic strategy for chemo-resistant B-ALL as a novel therapeutic approach for resistant disease.

Anatomical, subset, and HIV-dependent expression of viral sensors and restriction factors

We developed viral sensor and restriction factor-cytometry by time of flight (VISOR-CyTOF), which profiles 19 viral sensors and restriction factors (VISORs) simultaneously in single cells, and applied it to 41 postmortem tissues from people with HIV. Mucosal myeloid cells are well equipped with SAMHD1 and sensors of viral capsid and DNA while CD4+ T cells are not. In lymph node CD4+ Tfh, VISOR expression patterns reflect those favoring integration but blocking HIV gene expression, thus favoring viral latency. We also identify small subsets of bone marrow-, lung-, and gut-associated CD4+ T and myeloid cells expressing high levels of restriction factors targeting most stages of the HIV replication cycle. In vitro, HIV preferentially fuses to CD4+ T cells with a permissive VISOR profile, but early induction of select VISORs by T1IFN prevents productive HIV infection. Our findings document the diverse patterns of VISOR profiles across tissues and cellular subsets and define their association with susceptibility to HIV.

TET2-loss enhances immediate and time-resolved interferon-γ signaling responses across myeloid differentiation

Signaling responses to cytokines are disrupted in clonal hematopoiesis and myeloid malignancies. To better identify specific signaling response alterations in the presence or absence of TET2, we developed a 36-parameter cytometry by time-of-flight (CyTOF) panel of both surface marker and phosphoprotein antigens in murine bone marrow (BM). We show diverse, cell-type specific inflammatory cytokine responses in healthy hematopoietic cells. We next investigated changes associated with BM cells from Tet2KO mice. High-dimensional surface marker phenotyping revealed expansion of hematopoietic stem and progenitor cells (HSPCs), committed cKIT+Ly6C+ myeloid progenitors, and monocytes. Loss of TET2 function increased the magnitude of response to extracellular perturbations, including interferon (IFN)γ and H2O2. Response time courses revealed that IFNγ-mediated pSTAT1 remains elevated over time in Tet2KO. Further, IFNγ resulted in a more significant increase in major histocompatibility complex class II (MHCII) expression in Tet2KO immortalized progenitor cells than in Tet2WT. Inhibition of Janus kinase 1 and 2 (JAK1/2) with ruxolitinib significantly reduced STAT1 phosphorylation and MHCII expression in Tet2KO cells. Our results identify targetable disrupted signaling responses in Tet2KO cells.

Navigating the landscape of plant proteomics

In plants, proteins are fundamental to virtually all biological processes, such as photosynthesis, signal transduction, metabolic regulation, and stress responses. Studying protein distribution, function, modifications, and interactions at the cellular and tissue levels is critical for unraveling the complexities of these biological pathways. Protein abundance and localization are highly dynamic and vary widely across the proteome, presenting a challenge for global protein quantification and analysis. Mass spectrometry-based proteomics approaches have proven to be powerful tools for addressing this complex issue. In this review, we summarize recent advancements in proteomics research and their applications in plant biology, with an emphasis on the current state and challenges of studying post-translational modifications, single-cell proteomics, and protein-protein interactions. Additionally, we discuss future prospects for plant proteomics, highlighting potential opportunities that proteomics technologies offer in advancing plant biology research.

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

Advances in subpopulation separation and detection of extracellular vesicles: for liquid biopsy and downstream research

Extracellular vesicles (EVs) are carriers of a diverse array of bioactive molecules, making them valuable clinical tools for liquid biopsy in disease diagnosis and prognosis evaluation. These molecules play critical roles in various physiological and pathological conditions, and effective separation of EVs is essential to achieve these objectives. Due to the high heterogeneity of EVs, particularly with regard to their cargo molecules, merely isolating the general EV population is inadequate for liquid biopsy and biological function studies. Therefore, separating EV subpopulations becomes crucial. Traditional separation methods, such as differential ultracentrifugation and size exclusion chromatography, along with burgeoning techniques like classical microfluidic chips and covalent chemistry, often prove time-consuming, yield low purity, and have limited ability to address cargo heterogeneity. Thus, precise separation of EV subpopulations is of utmost importance. Additionally, detecting subpopulation-specific cargo is vital for validating the effectiveness of separation methods and supporting clinical biopsy applications. However, reviews that focus specifically on detection methods for EV subpopulations are limited. This paper provides a comprehensive overview of the methods for separating and detecting EV subpopulations with surface marker heterogeneity, comparing the advantages and limitations of each technique. Furthermore, it discusses challenges and future prospects for these methods in the context of liquid biopsy and downstream research. Collectively, this review aims to offer innovative insights into the separation and detection of EV subpopulations, guiding researchers to avoid common pitfalls and refine their investigative approaches.

Multiparameter quantitative analyses of diagnostic cells in brain tissues from tuberous sclerosis complex

The advent of high-dimensional imaging offers new opportunities to molecularly characterize diagnostic cells in disorders that have previously relied on histopathological definitions. One example case is found in tuberous sclerosis complex (TSC), a developmental disorder characterized by systemic growth of benign tumors. Within resected brain tissues from patients with TSC, detection of abnormally enlarged balloon cells (BCs) is pathognomonic for this disorder. Though BCs can be identified by an expert neuropathologist, little is known about the specificity and broad applicability of protein markers for these cells, complicating classification of proposed BCs identified in experimental models of this disorder. Here, we report the development of a customized machine learning pipeline (BAlloon IDENtifier; BAIDEN) that was trained to prospectively identify BCs in tissue sections using a histological stain compatible with high-dimensional cytometry. This approach was coupled to a custom 36-antibody panel and imaging mass cytometry (IMC) to explore the expression of multiple previously proposed BC marker proteins and develop a descriptor of BC features conserved across multiple tissue samples from patients with TSC. Here, we present a modular workflow encompassing BAIDEN, a custom antibody panel, a control sample microarray, and analysis pipelines-both open-source and in-house-and apply this workflow to understand the abundance, structure, and signaling activity of BCs as an example case of how high-dimensional imaging can be applied within human tissues.

A single-cell mass cytometry-based atlas of the developing mouse brain

Development of the mammalian brain requires precise molecular changes across diverse cell lineages. While single-cell RNA abundances in the developing brain have been characterized by single-cell RNA sequencing (scRNA-seq), single-cell protein abundances have not been characterized. To address this gap, we performed mass cytometry on the whole brain at embryonic day (E)11.5-E12.5 and the telencephalon, the diencephalon, the mesencephalon and the rhombencephalon at E13.5-postnatal day (P)4 from C57/BL6 mice. Using a 40-antibody panel to analyze 24,290,787 cells from two to four biological replicates per sample, we identify 85 molecularly distinct cell clusters from distinct lineages. Our analyses confirm canonical molecular pathways of neurogenesis and gliogenesis, and predict two distinct trajectories for cortical oligodendrogenesis. Differences in protein versus RNA expression from mass cytometry and scRNA-seq, validated by immunohistochemistry and RNAscope in situ hybridization (ISH), demonstrate the value of protein-level measurements for identifying functional cell states. Our findings show the utility of mass cytometry as a scalable platform for single-cell profiling of brain tissues.

Unveiling the power of high-dimensional cytometry data with cyCONDOR

High-dimensional cytometry (HDC) is a powerful technology for studying single-cell phenotypes in complex biological systems. Although technological developments and affordability have made HDC broadly available in recent years, technological advances were not coupled with an adequate development of analytical methods that can take full advantage of the complex data generated. While several analytical platforms and bioinformatics tools have become available for the analysis of HDC data, these are either web-hosted with limited scalability or designed for expert computational biologists, making their use unapproachable for wet lab scientists. Additionally, end-to-end HDC data analysis is further hampered due to missing unified analytical ecosystems, requiring researchers to navigate multiple platforms and software packages to complete the analysis. To bridge this data analysis gap in HDC we develop cyCONDOR, an easy-to-use computational framework covering not only all essential steps of cytometry data analysis but also including an array of downstream functions and tools to expand the biological interpretation of the data. The comprehensive suite of features of cyCONDOR, including guided pre-processing, clustering, dimensionality reduction, and machine learning algorithms, facilitates the seamless integration of cyCONDOR into clinically relevant settings, where scalability and disease classification are paramount for the widespread adoption of HDC in clinical practice. Additionally, the advanced analytical features of cyCONDOR, such as pseudotime analysis and batch integration, provide researchers with the tools to extract deeper insights from their data. We use cyCONDOR on a variety of data from different tissues and technologies demonstrating its versatility to assist the analysis of high-dimensional data from preprocessing to biological interpretation.

A CH4-Driven Ion Cloud-Stretched Approach Enables ICP-qMS for Multiplex Single-Cell Analysis

In the last 40 years, inductively coupled plasma quadrupole (q) mass spectrometry (ICP-qMS) has been recognized as one of the best tools for the quantification of multiple elements/isotopes and even the biomolecules they labeled in a homogeneous solution sample. However, it meets a tough challenge when acquiring multi-m/z signals from an intact single-cell dispersed in a cell suspension, since the single-cell ion cloud generated in ICP presents an intermittently transient event with a duration time of hundreds of microseconds while the dwell time plus settling time of the q is at the similar time scale when peak-hopping between different m/z. Herein, we report CH4 is able to stretch the single-cell ion cloud duration time to more than 7,000 μs in collision-reaction-cell (CRC), allowing multi-m/z signals acquisition by ICP-qMS. Quantification of single-cell's multiple phenotype protein markers can thus be achieved on ICP-(CH4-CRC)-qMS, not only revealing the heterogeneity between the single cells but also enabling an unambiguous cell-classification of their subtypes. CH4-driven ion cloud-stretched approach breaks through the long-standing bottleneck limited single-cell multiplex analysis on ICP-qMS, paving a path for more important applications of ICP-qMS in the fields related to single-cell analysis.

Zr-NMOF tagged with heterobifunctionalized aptamers for highly sensitive, multiplexed and rapid imaging mass cytometry

Imaging mass cytometry (IMC) permits high-dimensional single-cell spatial proteomics by harnessing mass tags to replace conventional fluorescence tags. However, the current IMC technique commonly adopts metal-chelated polymer (MCP) tags, which are limited in sensitivity, multiplicity and data acquisition speed. Here, we demonstrate nanometal-organic framework (NMOF) tags, which could concurrently augment IMC's sensitivity, multiplicity, and acquisition speed. We designed and synthesized uniform-sized Zr-NMOFs (∼31 nm, PDI < 0.1) and then functionalized them with heterobifunctionalized aptamers containing phosphate groups and fluorescent moieties to generate Zr-NMOF_Aptamer probes. Such functionalization enabled direct ligand exchange with zirconium ions on Zr-NMOFs, thus allowing for concurrent fluorescence and mass signal acquisitions. The fluorescence signal enabled large-scale rapid imaging to quickly locate the region-of-interest, therefore significantly reducing IMC's blind scanning time and compensating for IMC's lower resolution. Meanwhile, the Zr-NMOF_Aptamer probe exhibited specific molecular recognition and a fourfold enhancement in signal amplification over the commercial MCP probe. Additionally, we showed that Zr-NMOF_Aptamer probes were compatible with commercial MCP probes for high-multiplex co-staining in IMC analysis. The Zr-NMOF_Aptamer probe represents a promising development of next-generation molecular probes for spatial proteomics with IMC.

Inertial and Deterministic Lateral Displacement Integrated Microfluidic Chips for Epithelial-Mesenchymal Transition Analysis

With the aim of efficiently sorting rare circulating tumor cells (CTCs) from blood and minimizing damage to CTCs during isolation, we constructed an inertia-assisted single-cell focusing generator (I-SCF) and a water droplet deterministic lateral displacement cell sorting (D-DLD) microfluidic system (IDIC) based on different sizes, the device is initially sorted by a continuous fluid swing and Dean flow-assisted helical micromixers, then flows through a droplet shaped DLD region, enabling single-cell focused sequencing and precise separation, improving cell separation efficiency (>95%) and purity, while ensuring a high single cells survival rate of more than 98.6%. Subsequently, breast cancer cell lines were run through our chip, and then the downstream epithelial-mesenchymal transition (EMT) process induced by TGF-β was detected, and the levels of three proteins, EpCAM, PD-L1, and N-cadherin, were analyzed to establish the relationship between PD-L1 and the EMT process. Compared with other analytical techniques such as the filtration method, the enrichment method and immunoaffinity capture methods, this method not only ensures the separation efficiency and purity, but also ensures the cell activity, and avoids missing the different results caused by the heterogeneity of CTCs due to the isolation of high purity (84.01%). The device has a high throughput processing capacity (5 mL of diluted whole blood/∼2.8 h). By using the chip, we can more easily and conveniently predict tumor stage and carry out cancer prevention and treatment in advance, and it is expected to be further developed into a clinical liquid biopsy technology in the future.

Single-cell proteomics delineates murine systemic immune response to blast lung injury

Victims of explosive events frequently suffer from blast lung injuries. Immune system has been implicated in the pathogenesis of this disease. However, systemic immune responses underlying the progression and recovery of injury repair remain poorly understood. Here, we depict the systemic landscape of immune dysregulation during blast lung injury and uncover immune recovery patterns. Single-cell analyses reveal dramatic changes in neutrophils, macrophages, monocytes, dendritic cells, and eosinophils after a gas explosion, along with early involvement of CD4 T, CD8 T, and Th17 cells. We demonstrate that myeloid cells primarily exert functions during the acute phase, while the spleen serves as an alternative source of granulocytes. Granulopoiesis is initiated in the bone marrow at a later stage during blast lung injury recovery, rather than at the acute stage. These findings contribute to a better understanding of the pathogenesis and provide valuable insights for potential immune interventions in blast lung injury.

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.

Detection of >400 Cluster of Differentiation Biomarkers and Pathway Proteins in Single Immune Cells by Cyclic Multiplex In Situ Tagging for Single-Cell Proteomic Studies

The identification and characterization of immune cell subpopulations are critical to reveal cell development throughout life and immune responses to environmental factors. Next-generation sequencing technologies have dramatically advanced single-cell genomics and transcriptomics for immune cell classification. However, gene expression is often not correlated with protein expression, and immunotyping is mostly accepted in protein format. Current single-cell proteomic technologies are either limited in multiplex capacity or not sensitive enough to detect the critical functional proteins. Herein, we present a single-cell cyclic multiplex in situ tagging (CycMIST) technology to simultaneously measure >400 proteins, a scale of >10 times than similar technologies. Such an ultrahigh multiplexity is achieved by reiterative staining of the single cells coupled with a MIST array for detection. This technology has been thoroughly validated through comparison with flow cytometry and fluorescence immunostaining techniques. Both peripheral blood mononuclear cells (PBMCs) and T cells are analyzed by the CycMIST technology, and almost the entire spectrum of cluster of differentiation (CD) surface markers has been measured. The landscape of fluctuation of CD protein expression in single cells has been uncovered by our technology. Further study found T cell activation signatures and protein-protein networks. This study represents the highest multiplexity of single immune cell marker measurement targeting functional proteins. With additional information from intracellular proteins of the same single cells, our technology can potentially facilitate mechanistic studies of immune responses under various disease conditions.

A cancer immunoprofiling strategy using mass spectrometry coupled with bioorthogonal cleavage

The accurate quantification of biomarkers is paramount in modern medicine, particularly in cancer where precise diagnosis is imperative for targeted therapy selection. In this paper we described a multiplexed analysis diagnostic approach based on cleavable MS-tagged antibodies. The technology uses MS-tag isotopologues and the sydnonimine-cyclooctyne click-and-release bioorthogonal reaction. In a proof of concept study, we demonstrated the potential of this approach for cancer cell immunoprofiling in culture cells, tissues and in vivo as well, thereby unveiling promising diagnostic avenues.

Introducing FISCAS, a Tool for the Effective Generation of Single Cell MALDI-MSI Data

We introduce Fluorescence Integrated Single-Cell Analysis Script (FISCAS), which combines fluorescence microscopy with MALDI-MSI to streamline single-cell analysis. FISCAS enables automated selection of tight measurement regions, thereby reducing the acquisition of off-target pixels, and makes use of established algorithms for cell segmentation and coregistration to rapidly compile single-cell spectra. MALDI-compatible staining of membranes, nuclei, and lipid droplets allows the collection of fluorescence data prior to the MALDI-MSI measurement on a timsTOF fleX MALDI-2. Usefulness of the software is demonstrated by the example of THP-1 cells during stimulated differentiation into macrophages at different time points. In this proof-of-principle study, FISCAS was used to automatically generate single-cell mass spectra along with a wide range of morphometric parameters for a total number of roughly 1300 cells collected at 24, 48, and 72 h after the onset of stimulation. Data analysis of the combined morphometric and single-cell mass spectrometry data shows significant molecular heterogeneity within the cell population at each time point, indicating an independent differentiation of each individual cell rather than a synchronized mechanism. Here, the grouping of cells based on their molecular phenotype revealed an overall clearer distinction of the different phases of differentiation into macrophages and delivered an increased number of lipid signals as possible markers compared with traditional bulk analysis. Utilizing the linkage between mass spectrometric data and fluorescence microscopy confirmed the expected positive correlation between lipid droplet staining and the overall signal for triacylglyceride (TG), demonstrating the usefulness of this multimodal approach.

Comprehensive identification, isolation, and culture of human breast cell types

Tissues are formed and shaped by cells of many different types and are orchestrated through countless interactions. Deciphering a tissue's biological complexity thus requires studying it at cell-level resolution, where molecular and biochemical features of different cell types can be explored and thoroughly dissected. Unfortunately, the lack of comprehensive methods to identify, isolate, and culture each cell type from many tissues has impeded progress. Here, we present a method for the breadth of cell types composing the human breast. Our goal has long been to understand the essence of each of these different breast cell types, to reveal the underlying biology explaining their intrinsic features, the consequences of interactions, and their contributions to the tissue. This biological exploration has required cell purification, deep-RNA sequencing, and a thorough dissection of the genes and pathways defining each cell type. While the molecular analysis is presented in an adjoining article, we present here an exhaustive cellular dissection of the human breast and explore its cellular composition and histological organization. Moreover, we introduce a novel FACS antibody panel and rigorous gating strategy capable of isolating each of the 12 major breast cell types to purity. Finally, we describe the creation of primary cell models from nearly every breast cell type-some the first of their kind-and submit these as critical tools for studying the dynamic cellular interactions within breast tissues and tumors. Together, this body of work delivers a unique perspective of the breast, revealing insights into its cellular, molecular, and biochemical composition.

Hereditary chronic neutrophilic leukemia in a four-generation family without transformation to acute leukemia

Chronic neutrophilic leukemia (CNL) is a rare myeloproliferative neoplasm (MPN) characterized by peripheral blood neutrophilia, marrow granulocyte hyperplasia, hepatosplenomegaly, and driver mutations in the colony-stimulating factor 3 receptor (CSF3R). Designation of activating CSF3R mutations as a defining genomic abnormality for CNL has led to increased recognition of the disease. However, the natural history of CNL remains poorly understood with most patients reported being of older age, lacking germline data, and having poor survival, in part due to transformation to acute leukemia. CSF3R driver mutations in most patients with CNL have been reported to be acquired, although rare cases of germline mutations have been described. Here, we report the largest pedigree to date with familial CNL, spanning four generations with affected family members ranging in age from 4 to 53 years, none of whom have transformed to acute leukemia. A heterozygous T618I CSF3R mutation was identified in peripheral blood and mesenchymal stromal cells from the proband and in all affected living family members, while the unaffected family members tested were homozygous wild type. We show that the T618I mutation also confers a survival advantage to neutrophils in an MCL1-dependent manner. Collectively, these data provide additional insights into the natural history of familial CNL arising from T618I CSF3R mutations and suggest that enhanced neutrophil survival also contributes to the high neutrophil count observed in patients with CNL.

DrugReSC: targeting disease-critical cell subpopulations with single-cell transcriptomic data for drug repurposing in cancer

The field of computational drug repurposing aims to uncover novel therapeutic applications for existing drugs through high-throughput data analysis. However, there is a scarcity of drug repurposing methods leveraging the cellular-level information provided by single-cell RNA sequencing data. To address this need, we propose DrugReSC, an innovative approach to drug repurposing utilizing single-cell RNA sequencing data, intending to target specific cell subpopulations critical to disease pathology. DrugReSC constructs a drug-by-cell matrix representing the transcriptional relationships between individual cells and drugs and utilizes permutation-based methods to assess drug contributions to cellular phenotypic changes. We demonstrate DrugReSC's superior performance compared to existing drug repurposing methods based on bulk or single-cell RNA sequencing data across multiple cancer case studies. In summary, DrugReSC offers a novel perspective on the utilization of single-cell sequencing data in drug repurposing methods, contributing to the advancement of precision medicine for cancer.

Deciphering breast cancer dynamics: insights from single-cell and spatial profiling in the multi-omics era

As one of the most common tumors in women, the pathogenesis and tumor heterogeneity of breast cancer have long been the focal point of research, with the emergence of tumor metastasis and drug resistance posing persistent clinical challenges. The emergence of single-cell sequencing (SCS) technology has introduced novel approaches for gaining comprehensive insights into the biological behavior of malignant tumors. SCS is a high-throughput technology that has rapidly developed in the past decade, providing high-throughput molecular insights at the individual cell level. Furthermore, the advent of multitemporal point sampling and spatial omics also greatly enhances our understanding of cellular dynamics at both temporal and spatial levels. The paper provides a comprehensive overview of the historical development of SCS, and highlights the most recent advancements in utilizing SCS and spatial omics for breast cancer research. The findings from these studies will serve as valuable references for future advancements in basic research, clinical diagnosis, and treatment of breast cancer.

Evaluating Nanoparticulate Vaccine Formulations for Effective Antigen Presentation and T-Cell Proliferation Using an In Vitro Overlay Assay

Inducing T lymphocyte (T-cell) activation and proliferation with specificity against a pathogen is crucial in vaccine formulation. Assessing vaccine candidates' ability to induce T-cell proliferation helps optimize formulation for its safety, immunogenicity, and efficacy. Our in-house vaccine candidates use microparticles (MPs) and nanoparticles (NPs) to enhance antigen stability and target delivery to antigen-presenting cells (APCs), providing improved immunogenicity. Typically, vaccine formulations are screened for safety and immunostimulatory effects using in vitro methods, but extensive animal testing is often required to assess immunogenic responses. We identified the need for a rapid, intermediate screening process to select promising candidates before advancing to expensive and time-consuming in vivo evaluations. In this study, an in vitro overlay assay system was demonstrated as an effective high-throughput preclinical testing method to evaluate the immunogenic properties of early-stage vaccine formulations. The overlay assay's effectiveness in testing particulate vaccine candidates for immunogenic responses has been evaluated by optimizing the carboxyfluorescein succinimidyl ester (CFSE) T-cell proliferation assay. DCs were overlaid with T-cells, allowing vaccine-stimulated DCs to present antigens to CFSE-stained T-cells. T-cell proliferation was quantified using flow cytometry on days 0, 1, 2, 4, and 6 upon successful antigen presentation. The assay was tested with nanoparticulate vaccine formulations targeting Neisseria gonorrhoeae (CDC F62, FA19, FA1090), measles, H1N1 flu prototype, canine coronavirus, and Zika, with adjuvants including Alhydrogel® (Alum) and AddaVax™. The assay revealed robust T-cell proliferation in the vaccine treatment groups, with variations between bacterial and viral vaccine candidates. A dose-dependent study indicated immune stimulation varied with antigen dose. These findings highlight the assay's potential to differentiate and quantify effective antigen presentation, providing valuable insights for developing and optimizing vaccine formulations.

Testing a Nanoparticle Reagent for Imaging Mass Cytometry

Mass cytometry (MC), a powerful single-cell analysis technique, has limitations in detecting low-abundance biomarkers. Nanoparticle (NP) reagents offer the potential for enhancing sensitivity by carrying large numbers of heavy metal isotopes. Here, we report NP reporters for imaging mass cytometry (IMC) based on NaYF4:Yb3+/Er3+ NPs. A two-step ligand exchange was used to coat NP surfaces with either methoxy-PEG2K-neridronate (PEG-Ner) and/or poly(sulfobetaine methacrylate)-neridronate (PSBMA-Ner). Both modifications provided long-term colloidal stability in PBS buffer. IMC measurements on tonsil tissue showed that PSBMA-Ner or a 1:1 mixture of PSBMA-Ner + PEG-Ner effectively suppressed nonspecific binding (NSB) at 2 × 1010 NPs/mL, unlike PEG-Ner alone. However, breast cancer tissue samples showed increased NSB at titers above 2 × 1010 NPs/mL. Reduced NSB with mixed PEG-Ner and PSBMA-Ner coatings opens the door for using heterobifunctional PEGs for the development of NP conjugates with bioaffinity agents, enabling more sensitive and specific MC analyses.

Mapping the cancer surface proteome in search of target antigens for immunotherapy

Immune-based therapeutic interventions recognizing proteins localized on the cell surface of cancer cells are emerging as a promising cancer treatment. Antibody-based therapies and engineered T cells are now approved by the Food and Drug Administration to treat some malignancies. These therapies utilize a few cell surface proteins highly expressed on cancer cells to release the negative regulation of immune activation that limits antitumor responses (e.g., PD-1, PD-L1, CTLA4) or to redirect the T cell specificity toward blood cancer cells (e.g., CD19 and B cell maturation antigen). One limitation preventing broader application of these novel therapeutic strategies to all cancer types is the lack of suitable target antigens for all indications owing in part to the challenges in identifying such targets. Ideal target antigens are cell surface proteins highly expressed on malignant cells and absent in healthy tissues. Technological advances in mass spectrometry, enrichment protocols, and computational tools for cell surface protein isolation and annotation have recently enabled comprehensive analyses of the cancer cell surface proteome, from which novel immunotherapeutic target antigens may emerge. Here, we review the most recent progress in this field.

Exploring the Expression and Function of T Cell Surface Markers Identified through Cellular Indexing of Transcriptomes and Epitopes by Sequencing

PURPOSE

By utilizing both protein and mRNA expression patterns, we can identify more detailed and diverse immune cells, providing insights into understanding the complex immune landscape in cancer ecosystems.

MATERIALS AND METHODS

This study was performed by obtaining publicly available Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) data of peripheral blood mononuclear cells (PBMCs) from the Gene Expression Omnibus database. A total of 94674 total cells were analyzed, of which 32412 were T cells. There were 228 protein features and 16262 mRNA features in the data. The Seurat package was used for quality control and preprocessing, principal component analysis was performed, and Uniform Manifold Approximation and Projection was used to visualize the clusters. Protein and mRNA levels in the CITE-seq were analyzed.

RESULTS

We observed that a subset of T cells in the clusters generated at the protein level divided better. By identifying mRNA markers that were highly correlated with the CD4 and CD8 proteins and cross-validating CD26 and CD99 markers using flow cytometry, we found that CD4+ and CD8+ T cells were better discriminated in PBMCs. Weighted Nearest Neighbor clustering results identified a previously unobserved T cell subset.

CONCLUSION

In this study, we used CITE-seq data to confirm that protein expression patterns could be used to identify cells more precisely. These findings will improve our understanding of the heterogeneity of immune cells in the future and provide valuable insights into the complexity of the immune response in health and disease.

Shedding Light on Intracellular Proteins using Flow Cytometry

Intracellular protein abundance is routinely measured in mammalian cells using population-based techniques such as western blotting which fail to capture single cell protein levels or using fluorescence microscopy which is although suitable for single cell protein detection but not for rapid analysis of large no. of cells. Flow cytometry offers rapid, high-throughput, multiparameter-based analysis of intracellular protein expression in statistically significant no. of cells at single cell resolution. In past few decades, customized assays have been developed for flow cytometric detection of specific intracellular proteins. This review discusses the scope of flow cytometry for intracellular protein detection in mammalian cells along with specific applications. Technological advancements to overcome the limitations of traditional flow cytometry for the same are also discussed.

Spatial immunophenotyping using multiplexed imaging of immune follicles in secondary lymphoid tissues

Secondary lymphoid organs (SLOs), including tonsils (TS), lymph nodes (LN), and Peyer's Patches, exhibit complementary immune functions. However, little is known about the spatial organization of immune cells and extracellular matrix (ECM) in the SLOs. Traditional imaging is limited to a few markers, confining our understanding of the differences between the SLOs. Herein, imaging mass cytometry addressed this gap by simultaneously profiling 25-plex proteins in SLO tissues at subcellular resolution. The antibody panel targeted immune, stromal, chemokine, epigenetic, and functional markers. For robust cell identification, a computational workflow SpatialVizPheno was developed to spatially phenotype 999,970 cells using two approaches, including manual gating and semi-supervised gating, iterative clustering, and annotation. LN exhibited the highest density of B cells while the intestinal tissues contained the highest proportion of regulatory and follicular helper T cells. SpatialVizPheno identified the most prevalent interaction between follicular dendritic cells and stromal cells (SCs), plasmablasts/plasma cells, and the SCs across the lymphoid tissues. Collagen-enriched regions were associated with the spatial orientation of B cell follicles in both TS and LN tissues, but not in intestinal lymphoid tissues. Such spatial differences of immunophenotypes and ECM in different SLO tissues can be used to quantify the relationship between cellular organization and ultimate immune responses.

Cell-type specific molecular expression levels by restricted-dimensional cytometry

BACKGROUND

Cytometric analysis has been commonly used to delineate distinct cell subpopulations among peripheral blood mononuclear cells by the differential expression of surface receptors. This capability has reached its apogee with high-dimensional approaches such as mass cytometry and spectral cytometry that include simultaneous assessment of 20-50 analytes. Unfortunately, this approach also engenders significant complexity with analytical and interpretational pitfalls.

METHODS

Here, we demonstrate a complementary approach with restricted-dimensionality to assess cell-type specific intracellular molecular expression levels at exceptional levels of precision. The expression of five analytes was individually assessed in four mononuclear cell-types from peripheral blood.

RESULTS

Distinctions in expression levels were seen between cell-types and between samples from different donor groups. Mononuclear cell-type specific molecular expression levels distinguished pregnant from nonpregnant women and G-CSF-treated from untreated persons. Additionally, the precision of our analysis was sufficient to quantify a novel relationship between two molecules-Rel A and translocator protein-by correlational analysis.

CONCLUSIONS

Restricted-dimensional cytometry can provide a complementary approach to define characteristics of cell-type specific intracellular protein and phosphoantigen expression in mononuclear cells.

scHiCyclePred: a deep learning framework for predicting cell cycle phases from single-cell Hi-C data using multi-scale interaction information

The emergence of single-cell Hi-C (scHi-C) technology has provided unprecedented opportunities for investigating the intricate relationship between cell cycle phases and the three-dimensional (3D) structure of chromatin. However, accurately predicting cell cycle phases based on scHi-C data remains a formidable challenge. Here, we present scHiCyclePred, a prediction model that integrates multiple feature sets to leverage scHi-C data for predicting cell cycle phases. scHiCyclePred extracts 3D chromatin structure features by incorporating multi-scale interaction information. The comparative analysis illustrates that scHiCyclePred surpasses existing methods such as Nagano_method and CIRCLET across various metrics including accuracy (ACC), F1 score, Precision, Recall, and balanced accuracy (BACC). In addition, we evaluate scHiCyclePred against the previously published CIRCLET using the dataset of complex tissues (Liu_dataset). Experimental results reveal significant improvements with scHiCyclePred exhibiting improvements of 0.39, 0.52, 0.52, and 0.39 over the CIRCLET in terms of ACC, F1 score, Precision, and Recall metrics, respectively. Furthermore, we conduct analyses on three-dimensional chromatin dynamics and gene features during the cell cycle, providing a more comprehensive understanding of cell cycle dynamics through chromatin structure. scHiCyclePred not only offers insights into cell biology but also holds promise for catalyzing breakthroughs in disease research. Access scHiCyclePred on GitHub at https:// github.com/HaoWuLab-Bioinformatics/ scHiCyclePred .

Mass cytometric single cell immune profiles of peripheral blood from acute myeloid leukemia patients in complete remission with measurable residual disease

Measurable residual disease (MRD) is detected in approximately a quarter of AML chemotherapy responders, serving as a predictor for relapse and shorter survival. Immunological control of residual disease is suggested to prevent relapse, but the mechanisms involved are not fully understood. We present a peripheral blood single cell immune profiling by mass cytometry using a 42-antibody panel with particular emphasis on markers of cellular immune response. Six healthy donors were compared with four AML patients with MRD (MRD+) in first complete remission (CR1MRD+). Three of four patients demonstrated a favorable genetic risk profile, while the fourth patient had an unfavorable risk profile (complex karyotype, TP53-mutation) and a high level of MRD. Unsupervised clustering using self-organizing maps and dimensional reduction analysis was performed for visualization and analysis of immune cell subsets. CD57+ natural killer (NK)-cell subsets were found to be less abundant in patients than in healthy donors. Both T and NK cells demonstrated elevated expression of activity and maturation markers (CD44, granzyme B, and phosho-STAT5 Y694) in patients. Although mass cytometry remains an expensive method with limited scalability, our data suggest the utility for employing a 42-plex profiling for cellular immune surveillance in whole blood, and possibly as a biomarker platform in future clinical trials. The findings encourage further investigations of single cell immune profiling in CR1MRD+ AML-patients.

Signal amplification by cyclic extension enables high-sensitivity single-cell mass cytometry

Mass cytometry uses metal-isotope-tagged antibodies to label targets of interest, which enables simultaneous measurements of ~50 proteins or protein modifications in millions of single cells, but its sensitivity is limited. Here, we present a signal amplification technology, termed Amplification by Cyclic Extension (ACE), implementing thermal-cycling-based DNA in situ concatenation in combination with 3-cyanovinylcarbazole phosphoramidite-based DNA crosslinking to enable signal amplification simultaneously on >30 protein epitopes. We demonstrate the utility of ACE in low-abundance protein quantification with suspension mass cytometry to characterize molecular reprogramming during the epithelial-to-mesenchymal transition as well as the mesenchymal-to-epithelial transition. We show the capability of ACE to quantify the dynamics of signaling network responses in human T lymphocytes. We further present the application of ACE in imaging mass cytometry-based multiparametric tissue imaging to identify tissue compartments and profile spatial aspects related to pathological states in polycystic kidney tissues.

Comprehensive Characterization of Islet Remodeling in Development and in Diabetes Using Mass Cytometry

The pancreatic islet is the functional and structural unit of the pancreatic endocrine portion. Islet remodeling occurs in both normal development and pathogenesis of type 1 (T1D) and type 2 diabetes (T2D). However, accurately quantifying changes in islet cellular makeup and hormone expressions poses significant challenges due to large intra- and inter-donor heterogeneity and the limited scalability of traditional methods such as immunostaining. The cytometry by time-of-flight (CyTOF) technology enables simultaneous quantification of more than 30 protein markers at single-cell resolution in a high-throughput fashion. Moreover, with distinct DNA and viability markers, single live cells can be explicitly selected in CyTOF. Here, leveraging the CyTOF data generated by the Human Pancreas Analysis Program, we characterized more than 12 million islet cells from 71 donors. Our data revealed continued age-related changes in islet endocrine cell compositions, but the maturity of endocrine cells is reached by 3 years of age. We also observed significant changes in beta cell numbers and key protein expressions, along with a significant increase in bihormonal cells in T1D donors. In contrast, T2D donors exhibited minimal islet remodeling events. Our data shine a light on the islet dynamics during development and diabetes pathogenesis and suggest divergent pathogenesis processes of T1D and T2D. Our comprehensive approach not only elucidates islet plasticity but also establishes a foundation for integrated CyTOF analysis in islet biology and beyond.

Single-cell mass cytometry in immunological skin diseases

Immune-related skin diseases represent a collective of dermatological disorders intricately linked to dysfunctional immune system processes. These conditions are primarily characterized by an immoderate activation of the immune system or deviant immune responses, involving diverse immune components including immune cells, antibodies, and inflammatory mediators. However, the precise molecular dysregulation underlying numerous individual cases of these diseases and unique subsets respond under disease conditions remains elusive. Comprehending the mechanisms and determinants governing the homeostasis and functionality of diseases could offer potential therapeutic opportunities for intervention. Mass cytometry enables precise and high-throughput quantitative measurement of proteins within individual cells by utilizing antibodies labeled with rare heavy metal isotopes. Imaging mass cytometry employs mass spectrometry to obtain spatial information on cell-to-cell interactions within tissue sections, simultaneously utilizing more than 40 markers. The application of single-cell mass cytometry presents a unique opportunity to conduct highly multiplexed analysis at the single-cell level, thereby revolutionizing our understanding of cell population heterogeneity and hierarchy, cellular states, multiplexed signaling pathways, proteolysis products, and mRNA transcripts specifically in the context of many autoimmune diseases. This information holds the potential to offer novel approaches for the diagnosis, prognostic assessment, and monitoring responses to treatment, thereby enriching our strategies in managing the respective conditions. This review summarizes the present-day utilization of single-cell mass cytometry in studying immune-related skin diseases, highlighting its advantages and limitations. This technique will become increasingly prevalent in conducting extensive investigations into these disorders, ultimately yielding significant contributions to their accurate diagnosis and efficacious therapeutic interventions.

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.

Metal-detection based techniques and their applications in metallobiology

Metals are essential for human health and play a crucial role in numerous biological processes and pathways. Gaining a deeper insight into these biological events will facilitate novel strategies for disease prevention, early detection, and personalized treatment. In recent years, there has been significant progress in the development of metal-detection based techniques from single cell metallome and proteome profiling to multiplex imaging, which greatly enhance our comprehension of the intricate roles played by metals in complex biological systems. This perspective summarizes the recent progress in advanced metal-detection based techniques and highlights successful applications in elucidating the roles of metals in biology and medicine. Technologies including machine learning that couple with single-cell analysis such as mass cytometry and their application in metallobiology, cancer biology and immunology are also emphasized. Finally, we provide insights into future prospects and challenges involved in metal-detection based techniques, with the aim of inspiring further methodological advancements and applications that are accessible to chemists, biologists, and clinicians.

Astrocytic stress response is induced by exposure to astrocyte-binding antibodies expressed by plasmablasts from pediatric patients with acute transverse myelitis

BACKGROUND

Pediatric acute transverse myelitis (ATM) accounts for 20-30% of children presenting with a first acquired demyelinating syndrome (ADS) and may be the first clinical presentation of a relapsing ADS such as multiple sclerosis (MS). B cells have been strongly implicated in the pathogenesis of adult MS. However, little is known about B cells in pediatric MS, and even less so in pediatric ATM. Our lab previously showed that plasmablasts (PB), the earliest B cell subtype producing antibody, are expanded in adult ATM, and that these PBs produce self-reactive antibodies that target neurons. The goal of this study was to examine PB frequency and phenotype, immunoglobulin selection, and B cell receptor reactivity in pediatric patients presenting with ATM to gain insight to B cell involvement in disease.

METHODS

We compared the PB frequency and phenotype of 5 pediatric ATM patients and 10 pediatric healthy controls (HC) and compared them to previously reported adult ATM patients using cytometric data. We purified bulk IgG from the plasma samples and cloned 20 recombinant human antibodies (rhAbs) from individual PBs isolated from the blood. Plasma-derived IgG and rhAb autoreactivity was measured by mean fluorescence intensity (MFI) in neurons and astrocytes of murine brain or spinal cord and primary human astrocytes. We determined the potential impact of these rhAbs on astrocyte health by measuring stress and apoptotic response.

RESULTS

We found that pediatric ATM patients had a reduced frequency of peripheral blood PB. Serum IgG autoreactivity to neurons in EAE spinal cord was similar in the pediatric ATM patients and HC. However, serum IgG autoreactivity to astrocytes in EAE spinal cord was reduced in pediatric ATM patients compared to pediatric HC. Astrocyte-binding strength of rhAbs cloned from PBs was dependent on somatic hypermutation accumulation in the pediatric ATM cohort, but not HC. A similar observation in predilection for astrocyte binding over neuron binding of individual antibodies cloned from PBs was made in EAE brain tissue. Finally, exposure of human primary astrocytes to these astrocyte-binding antibodies increased astrocytic stress but did not lead to apoptosis.

CONCLUSIONS

Discordance in humoral immune responses to astrocytes may distinguish pediatric ATM from HC.

Development of a Spectral Flow Cytometry Analysis Pipeline for High-Dimensional Immune Cell Characterization

Flow cytometry is a widely used technique for immune cell analysis, offering insights into cell composition and function. Spectral flow cytometry allows for high-dimensional analysis of immune cells, overcoming limitations of conventional flow cytometry. However, analyzing data from large antibody panels can be challenging using traditional bi-axial gating strategies. Here, we present a novel analysis pipeline designed to improve analysis of spectral flow cytometry. We employ this method to identify rare T cell populations in aging. We isolated splenocytes from young (2-3 months) and aged (18-19 months) female mice then stained these with a panel of 20 fluorescently labeled antibodies. Spectral flow cytometry was performed, followed by data processing and analysis using Python within a Jupyter Notebook environment to perform batch correction, unsupervised clustering, dimensionality reduction, and differential expression analysis. Our analysis of 3,776,804 T cells from 11 spleens revealed 34 distinct T cell clusters identified by surface marker expression. We observed significant differences between young and aged mice, with certain clusters enriched in one age group over the other. Naïve, effector memory, and central memory CD8+ and CD4+ T cell subsets exhibited age-associated changes in abundance and marker expression. Additionally, γδ T cell clusters showed differential abundance between age groups. By leveraging high-dimensional analysis methods borrowed from single-cell RNA sequencing analysis, we identified age-related differences in T cell subsets, providing insights into the immune aging process. This approach offers a robust, free, and easily implemented analysis pipeline for spectral flow cytometry data that may facilitate the discovery of novel therapeutic targets for age-related immune dysfunction.

Mapping the cellular biogeography of human bone marrow niches using single-cell transcriptomics and proteomic imaging

Non-hematopoietic cells are essential contributors to hematopoiesis. However, heterogeneity and spatial organization of these cells in human bone marrow remain largely uncharacterized. We used single-cell RNA sequencing (scRNA-seq) to profile 29,325 non-hematopoietic cells and discovered nine transcriptionally distinct subtypes. We simultaneously profiled 53,417 hematopoietic cells and predicted their interactions with non-hematopoietic subsets. We employed co-detection by indexing (CODEX) to spatially profile over 1.2 million cells. We integrated scRNA-seq and CODEX data to link predicted cellular signaling with spatial proximity. Our analysis revealed a hyperoxygenated arterio-endosteal neighborhood for early myelopoiesis, and an adipocytic localization for early hematopoietic stem and progenitor cells (HSPCs). We used our CODEX atlas to annotate new images and uncovered mesenchymal stromal cell (MSC) expansion and spatial neighborhoods co-enriched for leukemic blasts and MSCs in acute myeloid leukemia (AML) patient samples. This spatially resolved, multiomic atlas of human bone marrow provides a reference for investigation of cellular interactions that drive hematopoiesis.

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.

Solving the Puzzle of Preterm Birth

Solving the puzzle of preterm birth has been challenging and will require novel integrative solutions as preterm birth likely arises from many etiologies. It has been demonstrated that many sociodemographic and psychological determinants of preterm birth relate to its complex biology. It is this understanding that has enabled the development of a novel preventative strategy, which integrates the omics profile (genome, epigenome, transcriptome, proteome, metabolome, microbiome) with sociodemographic, environmental, and psychological determinants of individual pregnant people to solve the puzzle of preterm birth.

Immune profiling of patients with extranodal natural killer/T cell lymphoma treated with daratumumab

Natural killer/T cell lymphoma (NKTCL) is a highly aggressive, heterogeneous non-Hodgkin lymphoma resulting from malignant proliferation of cytotoxic natural killer (NK) or T cells. Previous studies demonstrated variable expression of CD38 on NKTCL tumors. Daratumumab, a human IgGκ monoclonal antibody targeting CD38 with a direct on-tumor and immunomodulatory mechanism of action, was hypothesized to be a novel therapeutic option for patients with relapsed or refractory (R/R) NKTCL. In the phase 2 NKT2001 study (ClinicalTrials.gov Identifier: NCT02927925) assessing the safety and efficacy of daratumumab, a suboptimal overall response rate was seen in R/R NKTCL patients. One patient, whose tumors did not express CD38, responded to treatment, suggesting that the immunomodulatory activities of daratumumab may be sufficient to confer clinical benefit. To understand the suboptimal response rate and short duration of response, we investigated the immune profile of NKTCL patients from NKT2001 in the context of daratumumab anti-tumor activity. Tumor tissue and whole blood were, respectively, analyzed for CD38 expression and patient immune landscapes, which were assessed via cytometry by time-of-flight (CyTOF), multiparameter flow cytometry (MPFC), clonal sequencing, and plasma Epstein-Barr virus (EBV)-DNA level measurements. Changes observed in the immune profiles of NKTCL patients from NKT2001, including differences in B and T cell populations between responders and nonresponders, suggest that modulation of the immune environment is crucial for daratumumab anti-tumor activities in NKTCL. In conclusion, these findings highlight that the clinical benefit of daratumumab in NKTCL may be enriched by B/T cell-related biomarkers.

IL-1β Inhibition Partially Negates the Beneficial Effects of Diet-Induced Atherosclerosis Regression in Mice

BACKGROUND

Thromboembolic events secondary to rupture or erosion of advanced atherosclerotic lesions is the global leading cause of death. The most common and effective means to reduce these major adverse cardiovascular events, including myocardial infarction and stroke, is aggressive lipid lowering via a combination of drugs and dietary modifications. However, we know little regarding the effects of reducing dietary lipids on the composition and stability of advanced atherosclerotic lesions, the mechanisms that regulate these processes, and what therapeutic approaches might augment the benefits of lipid lowering.

METHODS

Smooth muscle cell lineage-tracing Apoe-/- mice were fed a high-cholesterol Western diet for 18 weeks and then a zero-cholesterol standard laboratory diet for 12 weeks before treating them with an IL (interleukin)-1β or control antibody for 8 weeks. We assessed lesion size and remodeling indices, as well as the cellular composition of aortic and brachiocephalic artery lesions, indices of plaque stability, overall plaque burden, and phenotypic transitions of smooth muscle cell and other lesion cells by smooth muscle cell lineage tracing combined with single-cell RNA sequencing, cytometry by time-of-flight, and immunostaining plus high-resolution confocal microscopic z-stack analysis.

RESULTS

Lipid lowering by switching Apoe-/- mice from a Western diet to a standard laboratory diet reduced LDL cholesterol levels by 70% and resulted in multiple beneficial effects including reduced overall aortic plaque burden, as well as reduced intraplaque hemorrhage and necrotic core area. However, contrary to expectations, IL-1β antibody treatment after diet-induced reductions in lipids resulted in multiple detrimental changes including increased plaque burden and brachiocephalic artery lesion size, as well as increasedintraplaque hemorrhage, necrotic core area, and senescence as compared with IgG control antibody-treated mice. Furthermore, IL-1β antibody treatment upregulated neutrophil degranulation pathways but downregulated smooth muscle cell extracellular matrix pathways likely important for the protective fibrous cap.

CONCLUSIONS

Taken together, IL-1β appears to be required for the maintenance of standard laboratory diet-induced reductions in plaque burden and increases in multiple indices of plaque stability.

Identification of clinically relevant subsets CD39+PD-1+CD8+ T cells and CD39+ regulatory T cells in intrahepatic cholangiocarcinoma using single-cell CyTOF

Intrahepatic cholangiocarcinoma (iCCA) is an aggressive liver malignancy with limited treatment options and a dismal prognosis. The tumor immune microenvironment (TIME) is crucial for iCCA progression, yet its comprehensive characterization remains incomplete. This study utilized mass cytometry by time of flight (CyTOF) to comprehensively analyze immune cell populations in fresh iCCA tumor samples and adjacent peritumor liver tissues. Notably, NK cell percentages significantly decreased in iCCA lesions compared to peritumor liver tissues. Conversely, an enrichment of immunosuppressive CD39+Foxp3+CD4+ regulatory T cells (CD39+T-regs) and exhausted-like CD8+T cells (with pronounced CD39 and PD-1 expression) within TIME was identified and confirmed by multiplex immunofluorescence staining in an independent patient cohort (n = 140). Crucially, tumor-infiltrating CD39+T-regs and CD39+PD-1+CD8+T cells emerged as independent prognostic indicators associated with an unfavorable prognosis in iCCA. These findings unveil the intricate immune landscape within iCCA, offering valuable insights for disease management and novel cancer immunotherapies.

iGATE analysis improves the interpretability of single-cell immune landscape of influenza infection

Influenza poses a persistent health burden worldwide. To design equitable vaccines effective across all demographics, it is essential to better understand how host factors such as genetic background and aging affect the single-cell immune landscape of influenza infection. Cytometry by time-of-flight (CyTOF) represents a promising technique in this pursuit, but interpreting its large, high-dimensional data remains difficult. We have developed a new analytical approach, in silico gating annotating training elucidating (iGATE), based on probabilistic support vector machine classification. By rapidly and accurately "gating" tens of millions of cells in silico into user-defined types, iGATE enabled us to track 25 canonical immune cell types in mouse lung over the course of influenza infection. Applying iGATE to study effects of host genetic background, we show that the lower survival of C57BL/6 mice compared with BALB/c was associated with a more rapid accumulation of inflammatory cell types and decreased IL-10 expression. Furthermore, we demonstrate that the most prominent effect of aging is a defective T cell response, reducing survival of aged mice. Finally, iGATE reveals that the 25 canonical immune cell types exhibited differential influenza infection susceptibility and replication permissiveness in vivo, but neither property varied with host genotype or aging. The software is available at https://github.com/UmichWenLab/iGATE.