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

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.

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 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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

Direct comparison of mass cytometry and single-cell RNA sequencing of human peripheral blood mononuclear cells

Single-cell methods offer a high-resolution approach for characterizing cell populations. Many studies rely on single-cell transcriptomics to draw conclusions regarding cell state and behavior, with the underlying assumption that transcriptomic readouts largely parallel their protein counterparts and subsequent activity. However, the relationship between transcriptomic and proteomic measurements is imprecise, and thus datasets that probe the extent of their concordance will be useful to refine such conclusions. Additionally, novel single-cell analysis tools often lack appropriate gold standard datasets for the purposes of assessment. Integrative (combining the two data modalities) and predictive (using one modality to improve results from the other) approaches in particular, would benefit from transcriptomic and proteomic data from the same sample of cells. For these reasons, we performed single-cell RNA sequencing, mass cytometry, and flow cytometry on a split-sample of human peripheral blood mononuclear cells. We directly compare the proportions of specific cell types resolved by each technique, and further describe the extent to which protein and mRNA measurements correlate within distinct cell types.

Payload-delivering engineered γδ T cells display enhanced cytotoxicity, persistence, and efficacy in preclinical models of osteosarcoma

T cell-based cancer immunotherapy has typically relied on membrane-bound cytotoxicity enhancers such as chimeric antigen receptors expressed in autologous αβ T cells. These approaches are limited by tonic signaling of synthetic constructs and costs associated with manufacturing. γδ T cells are an emerging alternative for cellular therapy, having innate antitumor activity, potent antibody-dependent cellular cytotoxicity, and minimal alloreactivity. We present an immunotherapeutic platform technology built around the innate properties of the Vγ9Vδ2 T cell, harnessing specific characteristics of this cell type and offering an allocompatible cellular therapy that recruits bystander immunity. We engineered γδ T cells to secrete synthetic tumor-targeting opsonins in the form of an scFv-Fc fusion protein and a mitogenic IL-15Rα-IL-15 fusion protein (stIL15). Using GD2 as a model antigen, we show that GD2-specific opsonin-secreting Vγ9Vδ2 T cells (stIL15-OPS-γδ T cells) have enhanced cytotoxicity and promote bystander activity of other lymphoid and myeloid cells. Secretion of stIL-15 abrogated the need for exogenous cytokine supplementation and further mediated activation of bystander natural killer cells. Compared with unmodified γδ T cells, stIL15-OPS-γδ T cells exhibited superior in vivo control of subcutaneous tumors and persistence in the blood. Moreover, stIL15-OPS-γδ T cells were efficacious against patient-derived osteosarcomas in animal models and in vitro, where efficacy could be boosted with the addition of zoledronic acid. Together, the data identify stIL15-OPS-γδ T cells as a candidate allogeneic cell therapy platform combining direct cytolysis with bystander activation to promote tumor control.

DNA Walker-Driven Mass Nanotag Assembly System for Simultaneously Profiling Dual Markers of Oxidative Stress at Different Cellular Locations

Simultaneous profiling of redox-regulated markers at different cellular sublocations is of great significance for unraveling the upstream and downstream molecular mechanisms of oxidative stress in living cells. Herein, by synchronizing dual target-triggered DNA machineries in one nanoentity, we engineered a DNA walker-driven mass nanotag (MNT) assembly system (w-MNT-AS) that can be sequentially activated by oxidative stress-associated mucin 1 (MUC1) and apurinic/apyrimidinic endonuclease 1 (APE1) from plasma membrane to cytoplasm and induce recycled assembly of MNTs for multiplex detection of the two markers by matrix-assisted laser desorption ionization mass spectrometry (MALDI MS). In the working cascade, the sensing process governs the separate activation of w-MNT-AS by MUC1 and APE1 in diverse locations, while the assembly process contributes to the parallel amplification of the ion signal of the characteristic mass tags. In this manner, the differences between MCF-7, HeLa, HepG2, and L02 cells in membrane MUC1 expression and cytoplasmic APE1 activation were fully characterized. Furthermore, the oxidative stress level and dynamics caused by exogenous H2O2, doxorubicin, and simvastatin were comprehensively demonstrated by tracking the fate of the two markers across different cellular locations. The proposed w-MNT-AS coupled MS method provides an effective route to probe multiple functional molecules that lie at different locations while participating in the same cellular event, facilitating the mechanistic studies on cellular response to oxidative stress and other disease-related cellular processes.

Single Cell Profiling Distinguishes Leukemia-Selective Chemotypes

A central challenge in chemical biology is to distinguish molecular families in which small structural changes trigger large changes in cell biology. Such families might be ideal scaffolds for developing cell-selective chemical effectors - for example, molecules that activate DNA damage responses in malignant cells while sparing healthy cells. Across closely related structural variants, subtle structural changes have the potential to result in contrasting bioactivity patterns across different cell types. Here, we tested a 600-compound Diversity Set of screening molecules from the Boston University Center for Molecular Discovery (BU-CMD) in a novel phospho-flow assay that tracked fundamental cell biological processes, including DNA damage response, apoptosis, M-phase cell cycle, and protein synthesis in MV411 leukemia cells. Among the chemotypes screened, synthetic congeners of the rocaglate family were especially bioactive. In follow-up studies, 37 rocaglates were selected and deeply characterized using 12 million additional cellular measurements across MV411 leukemia cells and healthy peripheral blood mononuclear cells. Of the selected rocaglates, 92% displayed significant bioactivity in human cells, and 65% selectively induced DNA damage responses in leukemia and not healthy human blood cells. Furthermore, the signaling and cell-type selectivity were connected to structural features of rocaglate subfamilies. In particular, three rocaglates from the rocaglate pyrimidinone (RP) structural subclass were the only molecules that activated exceptional DNA damage responses in leukemia cells without activating a detectable DNA damage response in healthy cells. These results indicate that the RP subset should be extensively characterized for anticancer therapeutic potential as it relates to the DNA damage response. This single cell profiling approach advances a chemical biology platform to dissect how systematic variations in chemical structure can profoundly and differentially impact basic functions of healthy and diseased cells.

Photosensitive Hydrogel with Temperature-Controlled Reversible Nano-Apertures for Single-Cell Protein Analysis

Single cell western blot (scWB) is one of the most important methods for cellular heterogeneity profiling. However, current scWB based on conventional photoactive polyacrylamide hydrogel material suffers from the tradeoff between in-gel probing and separation resolution. Here, a highly sensitive temperature-controlled single-cell western blotting (tc-scWB) method is introduced, which is based on a thermo/photo-dualistic-sensitive polyacrylamide hydrogel, namely acrylic acid-functionalized graphene oxide (AFGO) assisted, N-isopropylacrylamide modified polyacrylamide (ANP) hydrogel. The ANP hydrogel is contracted at high-temperature to constrain protein band diffusion during microchip electrophoretic separation, while the gel aperture is expanded under low-temperature for better antibody penetration into the hydrogel. The tc-scWB method enables the separation and profiling of small-molecule-weight proteins with highly crosslinked gel (12% T) in SDS-PAGE. The tc-scWB is demonstrated on three metabolic and ER stress-specific proteins (CHOP, MDH2 and FH) in four pancreatic cell subtypes, revealing the expression of key enzymes in the Krebs cycle is upregulated with enhanced ER stress. It is found that ER stress can regulate crucial enzyme (MDH2 and FH) activities of metabolic cascade in cancer cells, boosting aerobic respiration to attenuate the Warburg effect and promote cell apoptosis. The tc-scWB is a general toolbox for the analysis of low-abundance small-molecular functional proteins at the single-cell level.

High-throughput single-cell mass spectrometry enables metabolic network analysis by resolving phospholipid C[double bond, length as m-dash]C isomers

Single-cell mass spectrometry (MS) is an essential technology for sensitive and multiplexed analysis of metabolites and lipids for cell phenotyping and pathway studies. However, the structural elucidation of lipids from single cells remains a challenge, especially in the high-throughput scenario. Technically, there is a contradiction between the inadequate sample amount (i.e. a single cell, 0.5-20 pL) for replicate or multiple analysis, on the one hand, and the high metabolite coverage and multidimensional structure analysis that needs to be performed for each single cell, on the other hand. Here, we have developed a high-throughput single-cell MS platform that can perform both lipid profiling and lipid carbon-carbon double bond (C[double bond, length as m-dash]C) location isomer resolution analysis, aided by C[double bond, length as m-dash]C activation in unsaturated lipids by the Paternò-Büchi (PB) reaction and tandem MS, termed single-cell structural lipidomics analysis. The method can achieve a single-cell analysis throughput of 51 cells per minute. A total of 145 lipids were structurally characterized at the subclass level, of which the relative abundance of 17 isomeric lipids differing in the location of C[double bond, length as m-dash]C from 5 lipid precursors was determined. While cell-to-cell variations in MS1-based lipid profiling can be large, an advantage of quantifying lipid C[double bond, length as m-dash]C location isomers is the significantly improved quantitation accuracy. For example, the relative standard deviations (RSDs) of the relative amounts of PC 34:1 C[double bond, length as m-dash]C position isomers in MDA-MB-468 cells are half smaller than those measured for PC 34:1 as a whole by MS1 abundance profiling. Taken together, the developed method can be effectively used for in-depth structural lipid metabolism network analysis by high-throughput analysis of 142 MDA-MB-468 human breast cancer cells.

Label-free detection of biomolecules using inductively coupled plasma mass spectrometry (ICP-MS)

Bioassays using inductively coupled plasma mass spectrometry (ICP-MS) have gained increasing attention because of the high sensitivity of ICP-MS and the various strategies of labeling biomolecules with detectable metal tags. The classic strategy to tag the target biomolecules is through direct antibody-antigen interaction and DNA hybridization, and requires the separation of the bound from the unbound tags. Label-free ICP-MS techniques for biomolecular assays do not require direct labeling: they generate detectable metal ions indirectly from specific biomolecular reactions, such as enzymatic cleavage. Here, we highlight the development of three main strategies of label-free ICP-MS assays for biomolecules: (1) enzymatic cleavage of metal-labeled substrates, (2) release of immobilized metal ions from the DNA backbone, and (3) nucleic acid amplification-assisted aggregation and release of metal tags to achieve amplified detection. We briefly describe the fundamental basis of these label-free ICP-MS assays and discuss the benefits and drawbacks of various designs. Future research is needed to reduce non-specific adsorption and minimize background and interference. Analytical innovations are also required to confront challenges faced by in vivo applications.

Harnessing the potential of reverse-phase protein array technology: Advancing precision oncology strategies

The last few decades have seen remarkable strides in the field of cancer therapy. Precision oncology coupled with comprehensive genomic profiling has become routine clinical practice for solid tumors, the advent of immune checkpoint inhibitors has transformed the landscape of oncology treatment, and the number of cancer drug approvals has continued to increase. Nevertheless, the application of genomics-driven precision oncology has thus far benefited only 10%-20% of cancer patients, leaving the majority without matched treatment options. This limitation underscores the need to explore alternative avenues with regard to selecting patients for targeted therapies. In contrast with genomics-based approaches, proteomics-based strategies offer a more precise understanding of the intricate biological processes driving cancer pathogenesis. This perspective underscores the importance of integrating complementary proteomic analyses into the next phase of precision oncology to establish robust biomarker-drug associations and surmount challenges related to drug resistance. One promising technology in this regard is the reverse-phase protein array (RPPA), which excels in quantitatively detecting protein modifications, even with limited amounts of sample. Its cost-effectiveness and rapid turnaround time further bolster its appeal for application in clinical settings. Here, we review the current status of genomics-driven precision oncology, as well as its limitations, with an emphasis on drug resistance. Subsequently, we explore the application of RPPA technology as a catalyst for advancing precision oncology. Through illustrative examples drawn from clinical trials, we demonstrate its utility for unraveling the molecular mechanisms underlying drug responses and resistance.

One-Shot Single-Cell Proteome and Metabolome Analysis Strategy for the Same Single Cell

Characterizing the profiles of proteome and metabolome at the single-cell level is of great significance in single-cell multiomic studies. Herein, we proposed a novel strategy called one-shot single-cell proteome and metabolome analysis (scPMA) to acquire the proteome and metabolome information in a single-cell individual in one injection of LC-MS/MS analysis. Based on the scPMA strategy, a total workflow was developed to achieve the single-cell capture, nanoliter-scale sample pretreatment, one-shot LC injection and separation of the enzyme-digested peptides and metabolites, and dual-zone MS/MS detection for proteome and metabolome profiling. Benefiting from the scPMA strategy, we realized dual-omic analysis of single tumor cells, including A549, HeLa, and HepG2 cells with 816, 578, and 293 protein groups and 72, 91, and 148 metabolites quantified on average. A single-cell perspective experiment for investigating the doxorubicin-induced antitumor effects in both the proteome and metabolome aspects was also performed.

SuperCellCyto: enabling efficient analysis of large scale cytometry datasets

Advancements in cytometry technologies have enabled quantification of up to 50 proteins across millions of cells at single cell resolution. Analysis of cytometry data routinely involves tasks such as data integration, clustering, and dimensionality reduction. While numerous tools exist, many require extensive run times when processing large cytometry data containing millions of cells. Existing solutions, such as random subsampling, are inadequate as they risk excluding rare cell subsets. To address this, we propose SuperCellCyto, an R package that builds on the SuperCell tool which groups highly similar cells into supercells. SuperCellCyto is available on GitHub ( https://github.com/phipsonlab/SuperCellCyto ) and Zenodo ( https://doi.org/10.5281/zenodo.10521294 ).

Comparative assessment of cytometry by time-of-flight and full spectral flow cytometry based on a 33-color antibody panel

Mass cytometry and full spectrum flow cytometry have recently emerged as new promising single cell proteomic analysis tools that can be exploited to decipher the extensive diversity of immune cell repertoires and their implication in human diseases. In this study, we evaluated the performance of mass cytometry against full spectrum flow cytometry using an identical 33-color antibody panel on four healthy individuals. Our data revealed an overall high concordance in the quantification of major immune cell populations between the two platforms using a semi-automated clustering approach. We further showed a strong correlation of cluster assignment when comparing manual and automated clustering. Both comparisons revealed minor disagreements in the quantification and assignment of rare cell subpopulations. Our study showed that both single cell proteomic technologies generate highly overlapping results and substantiate that the choice of technology is not a primary factor for successful biological assessment of cell profiles but must be considered in a broader design framework of clinical studies.

Cell metabolism: Functional and phenotypic single cell approaches

Several metabolic pathways are essential for the physiological regulation of immune cells, but their dysregulation can cause immune dysfunction. Hypermetabolic and hypometabolic states represent deviations in the magnitude and flexibility of effector cells in different contexts, for example in autoimmunity, infections or cancer. To study immunometabolism, most methods focus on bulk populations and rely on in vitro activation assays. Nowadays, thanks to the development of single-cell technologies, including multiparameter flow cytometry, mass cytometry, RNA cytometry, among others, the metabolic state of individual immune cells can be measured in a variety of samples obtained in basic, translational and clinical studies. Here, we provide an overview of different single-cell approaches that are employed to investigate both mitochondrial functions and cell dependence from mitochondria metabolism. Moreover, besides the description of the appropriate experimental settings, we discuss the strengths and weaknesses of different approaches with the aim to suggest how to study cell metabolism in the settings of interest.

VIBRANT: spectral profiling for single-cell drug responses

High-content cell profiling has proven invaluable for single-cell phenotyping in response to chemical perturbations. However, methods with improved throughput, information content and affordability are still needed. We present a new high-content spectral profiling method named vibrational painting (VIBRANT), integrating mid-infrared vibrational imaging, multiplexed vibrational probes and an optimized data analysis pipeline for measuring single-cell drug responses. Three infrared-active vibrational probes were designed to measure distinct essential metabolic activities in human cancer cells. More than 20,000 single-cell drug responses were collected, corresponding to 23 drug treatments. The resulting spectral profile is highly sensitive to phenotypic changes under drug perturbation. Using this property, we built a machine learning classifier to accurately predict drug mechanism of action at single-cell level with minimal batch effects. We further designed an algorithm to discover drug candidates with new mechanisms of action and evaluate drug combinations. Overall, VIBRANT has demonstrated great potential across multiple areas of phenotypic screening.

Multiplex Profiling of Biomarker and Drug Uptake in Single Cells Using Microfluidic Flow Cytometry and Mass Spectrometry

To perform multiplex profiling of single cells and eliminate the risk of potential sample loss caused by centrifugation, we developed a microfluidic flow cytometry and mass spectrometry system (μCytoMS) to evaluate the drug uptake and induced protein expression at the single cell level. It involves a microfluidic chip for the alignment and purification of single cells followed by detection with laser-induced fluorescence (LIF) and inductively coupled plasma mass spectrometry (ICP-MS). Biofunctionalized nanoprobes (BioNPs), conjugating ∼3000 6-FAM-Sgc8 aptamers on a single gold nanoparticle (AuNP) (Kd = 0.23 nM), were engineered to selectively bind with protein tyrosine kinase 7 (PTK7) on target cells. PTK7 expression induced by oxaliplatin (OXA) uptake was assayed with LIF, while ICP-MS measurement of 195Pt revealed OXA uptake of the drug in individual cells, which provided further in-depth information about the drug in relation to PTK7 expression. At an ultralow flow of ∼0.043 dyn/cm2 (20 μL/min), the chip facilitates the extremely fast focusing of BioNPs labeled single cells without the need for centrifugal purification. It ensures multiplex profiling of single cells at a throughput speed of 500 cells/min as compared to 40 cells/min in previous studies. Using a machine learning algorithm to initially profile drug uptake and marker expression in tumor cell lines, μCytoMS was able to perform in situ profiling of the PTK7 response to the OXA at single-cell resolution for tests done on clinical samples from 10 breast cancer patients. It offers great potential for multiplex single-cell phenotypic analysis and clinical diagnosis.