Study on the mechanism of heterogeneous tumor-associated macrophages in three subtypes of breast cancer through the integration of single-cell RNA sequencing and in vitro experiments.

BACKGROUND: Tumor-associated macrophages (TAM) exert a significant influence on the progression and heterogeneity of various subtypes of breast cancer (BRCA). However, the roles of heterogeneous TAM within BRCA subtypes remain unclear. Therefore, this study sought to elucidate the role of TAM across the following three BRCA subtypes: triple-negative breast cancer, luminal, and HER2. MATERIALS AND METHODS: This investigation aimed to delineate the variations in marker genes, drug sensitivity, and cellular communication among TAM across the three BRCA subtypes. We identified specific ligand-receptor (L-R) pairs and downstream mechanisms regulated by VEGFA-VEGFR1, SPP1-CD44, and SPP1-ITGB1 L-R pairs. Experimental verification of these pairs was conducted by co-culturing macrophages with three subtypes of BRCA cells. RESULTS: Our findings reveal the heterogeneity of macrophages within the three BRCA subtypes, evidenced by variations in marker gene expression, composition, and functional characteristics. Notably, heterogeneous TAM were found to promote invasive migration and epithelial-mesenchymal transition (EMT) in MDA-MB-231, MCF-7, and SKBR3 cells, activating NF-κB pathway via P38 MAPK, TGF-ß1, and AKT, respectively, through distinct VEGFA-VEGFR1, SPP1-CD44, and SPP1-ITGB1 L-R pairs. Inhibition of these specific L-R pairs effectively reversed EMT, migration, and invasion of each cancer cells. Furthermore, we observed a correlation between ligand gene expression and TAM sensitivity to anticancer drugs, suggesting a potential strategy for optimizing personalized treatment guidance. CONCLUSION: Our study highlights the capacity of heterogeneous TAM to modulate biological functions via distinct pathways mediated by specific L-R pairs within diverse BRCA subtypes. This study might provide insights into precision immunotherapy of different subtypes of BRCA.

Systematic evaluation with practical guidelines for single-cell and spatially resolved transcriptomics data simulation under multiple scenarios.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) have led to groundbreaking advancements in life sciences. To develop bioinformatics tools for scRNA-seq and SRT data and perform unbiased benchmarks, data simulation has been widely adopted by providing explicit ground truth and generating customized datasets. However, the performance of simulation methods under multiple scenarios has not been comprehensively assessed, making it challenging to choose suitable methods without practical guidelines. RESULTS: We systematically evaluated 49 simulation methods developed for scRNA-seq and/or SRT data in terms of accuracy, functionality, scalability, and usability using 152 reference datasets derived from 24 platforms. SRTsim, scDesign3, ZINB-WaVE, and scDesign2 have the best accuracy performance across various platforms. Unexpectedly, some methods tailored to scRNA-seq data have potential compatibility for simulating SRT data. Lun, SPARSim, and scDesign3-tree outperform other methods under corresponding simulation scenarios. Phenopath, Lun, Simple, and MFA yield high scalability scores but they cannot generate realistic simulated data. Users should consider the trade-offs between method accuracy and scalability (or functionality) when making decisions. Additionally, execution errors are mainly caused by failed parameter estimations and appearance of missing or infinite values in calculations. We provide practical guidelines for method selection, a standard pipeline Simpipe ( https://github.com/duohongrui/simpipe ; https://doi.org/10.5281/zenodo.11178409 ), and an online tool Simsite ( https://www.ciblab.net/software/simshiny/ ) for data simulation. CONCLUSIONS: No method performs best on all criteria, thus a good-yet-not-the-best method is recommended if it solves problems effectively and reasonably. Our comprehensive work provides crucial insights for developers on modeling gene expression data and fosters the simulation process for users.

Single-cell and genome-wide Mendelian randomization identifies causative genes for gout.

BACKGROUND: Gout is a prevalent manifestation of metabolic osteoarthritis induced by elevated blood uric acid levels. The purpose of this study was to investigate the mechanisms of gene expression regulation in gout disease and elucidate its pathogenesis. METHODS: The study integrated gout genome-wide association study (GWAS) data, single-cell transcriptomics (scRNA-seq), expression quantitative trait loci (eQTL), and methylation quantitative trait loci (mQTL) data for analysis, and utilized two-sample Mendelian randomization study to comprehend the causal relationship between proteins and gout. RESULTS: We identified 17 association signals for gout at unique genetic loci, including four genes related by protein-protein interaction network (PPI) analysis: TRIM46, THBS3, MTX1, and KRTCAP2. Additionally, we discerned 22 methylation sites in relation to gout. The study also found that genes such as TRIM46, MAP3K11, KRTCAP2, and TM7SF2 could potentially elevate the risk of gout. Through a Mendelian randomization (MR) analysis, we identified three proteins causally associated with gout: ADH1B, BMP1, and HIST1H3A. CONCLUSION: According to our findings, gout is linked with the expression and function of particular genes and proteins. These genes and proteins have the potential to function as novel diagnostic and therapeutic targets for gout. These discoveries shed new light on the pathological mechanisms of gout and clear the way for future research on this condition.

A hybrid demultiplexing strategy that improves performance and robustness of cell hashing.

Cell hashing, a nucleotide barcode-based method that allows users to pool multiple samples and demultiplex in downstream analysis, has gained widespread popularity in single-cell sequencing due to its compatibility, simplicity, and cost-effectiveness. Despite these advantages, the performance of this method remains unsatisfactory under certain circumstances, especially in experiments that have imbalanced sample sizes or use many hashtag antibodies. Here, we introduce a hybrid demultiplexing strategy that increases accuracy and cell recovery in multi-sample single-cell experiments. This approach correlates the results of cell hashing and genetic variant clustering, enabling precise and efficient cell identity determination without additional experimental costs or efforts. In addition, we developed HTOreader, a demultiplexing tool for cell hashing that improves the accuracy of cut-off calling by avoiding the dominance of negative signals in experiments with many hashtags or imbalanced sample sizes. When compared to existing methods using real-world datasets, this hybrid approach and HTOreader consistently generate reliable results with increased accuracy and cell recovery.

Deciphering Müller cell heterogeneity signatures in diabetic retinopathy across species: an integrative single-cell analysis.

Diabetic retinopathy (DR), a leading cause of visual impairment, demands a profound comprehension of its cellular mechanisms to formulate effective therapeutic strategies. Our study presentes a comprehensive single-cell analysis elucidating the intricate landscape of Müller cells within DR, emphasizing their nuanced involvement. Utilizing scRNA-seq data from both Sprague-Dawley rat models and human patients, we delineated distinct Müller cell clusters and their corresponding gene expression profiles. These findings were further validated through differential gene expression analysis utilizing human transcriptomic data. Notably, certain Müller cell clusters displayed upregulation of the Rho gene, implying a phagocytic response to damaged photoreceptors within the DR microenvironment. This phenomenon was consistently observed across species. Additionally, the co-expression patterns of RHO and PDE6G within Müller cell clusters provided compelling evidence supporting their potential role in maintaining retinal integrity during DR. Our results offer novel insights into the cellular dynamics of DR and underscore Müller cells as promising therapeutic targets for preserving vision in retinal disorders induced by diabetes.

scCRT: a contrastive-based dimensionality reduction model for scRNA-seq trajectory inference.

Trajectory inference is a crucial task in single-cell RNA-sequencing downstream analysis, which can reveal the dynamic processes of biological development, including cell differentiation. Dimensionality reduction is an important step in the trajectory inference process. However, most existing trajectory methods rely on cell features derived from traditional dimensionality reduction methods, such as principal component analysis and uniform manifold approximation and projection. These methods are not specifically designed for trajectory inference and fail to fully leverage prior information from upstream analysis, limiting their performance. Here, we introduce scCRT, a novel dimensionality reduction model for trajectory inference. In order to utilize prior information to learn accurate cells representation, scCRT integrates two feature learning components: a cell-level pairwise module and a cluster-level contrastive module. The cell-level module focuses on learning accurate cell representations in a reduced-dimensionality space while maintaining the cell-cell positional relationships in the original space. The cluster-level contrastive module uses prior cell state information to aggregate similar cells, preventing excessive dispersion in the low-dimensional space. Experimental findings from 54 real and 81 synthetic datasets, totaling 135 datasets, highlighted the superior performance of scCRT compared with commonly used trajectory inference methods. Additionally, an ablation study revealed that both cell-level and cluster-level modules enhance the model's ability to learn accurate cell features, facilitating cell lineage inference. The source code of scCRT is available at https://github.com/yuchen21-web/scCRT-for-scRNA-seq.

scEWE: high-order element-wise weighted ensemble clustering for heterogeneity analysis of single-cell RNA-sequencing data.

With the emergence of large amount of single-cell RNA sequencing (scRNA-seq) data, the exploration of computational methods has become critical in revealing biological mechanisms. Clustering is a representative for deciphering cellular heterogeneity embedded in scRNA-seq data. However, due to the diversity of datasets, none of the existing single-cell clustering methods shows overwhelming performance on all datasets. Weighted ensemble methods are proposed to integrate multiple results to improve heterogeneity analysis performance. These methods are usually weighted by considering the reliability of the base clustering results, ignoring the performance difference of the same base clustering on different cells. In this paper, we propose a high-order element-wise weighting strategy based self-representative ensemble learning framework: scEWE. By assigning different base clustering weights to individual cells, we construct and optimize the consensus matrix in a careful and exquisite way. In addition, we extracted the high-order information between cells, which enhanced the ability to represent the similarity relationship between cells. scEWE is experimentally shown to significantly outperform the state-of-the-art methods, which strongly demonstrates the effectiveness of the method and supports the potential applications in complex single-cell data analytical problems.

Label-free metabolic optical biomarkers track stem cell fate transition in real time.

Tracking stem cell fate transition is crucial for understanding their development and optimizing biomanufacturing. Destructive single-cell methods provide a pseudotemporal landscape of stem cell differentiation but cannot monitor stem cell fate in real time. We established a metabolic optical metric using label-free fluorescence lifetime imaging microscopy (FLIM), feature extraction and machine learning-assisted analysis, for real-time cell fate tracking. From a library of 205 metabolic optical biomarker (MOB) features, we identified 56 associated with hematopoietic stem cell (HSC) differentiation. These features collectively describe HSC fate transition and detect its bifurcate lineage choice. We further derived a MOB score measuring the "metabolic stemness" of single cells and distinguishing their division patterns. This score reveals a distinct role of asymmetric division in rescuing stem cells with compromised metabolic stemness and a unique mechanism of PI3K inhibition in promoting ex vivo HSC maintenance. MOB profiling is a powerful tool for tracking stem cell fate transition and improving their biomanufacturing from a single-cell perspective.

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.

Spectral analysis and sorting of microbial organisms using a spectral sorter.

This chapter discusses the problems related to the application of conventional flow cytometers to microbiology. To address some of those limitations, the concept of spectral flow cytometry is introduced and the advantages over conventional flow cytometry for bacterial sorting are presented. We demonstrate by using ThermoFisher's Bigfoot spectral sorter where the spectral signatures of different stains for staining bacteria are demonstrated with an example of performing unmixing on spectral datasets. In addition to the Bigfoot's spectral analysis, the special biosafety features of this instrument are discussed. Utilizing these biosafety features, the sorting and patterning at the single cell level is optimized using non-pathogenic bacteria. Finally, the chapter is concluded by presenting a novel, label free, non-destructive, and rapid phenotypic method called Elastic Light Scattering (ELS) technology for identification of the patterned bacterial cells based on their unique colony scatter patterns.

Harmonized analysis of PBMC by mass cytometry.

Mass cytometry permits the high dimensional analysis of cellular systems at single-cell resolution with high throughput in various areas of biomedical research. Here, we provide a state-of-the-art protocol for the analysis of human peripheral blood mononuclear cells (PBMC) by mass cytometry. We focus on the implementation of measures promoting the harmonization of large and complex studies to aid robustness and reproducibility of immune phenotyping data.

Immunophenotyping challenging tissue types using high-dimensional full spectrum flow cytometry.

Technological advancements in fluorescence flow cytometry and an ever-expanding understanding of the complexity of the immune system, have led to the development of large flow cytometry panels, reaching up to 40 markers at the single-cell level. Full spectrum flow cytometry, that measures the full emission range of all the fluorophores present in the panel instead of only the emission peaks is now routinely used in many laboratories internationally, and the demand for this technology is rapidly increasing. With the capacity to use larger and more complex staining panels, optimized protocols are required for the best panel design, panel validation and high-dimensional data analysis outcomes. In addition, for ex vivo experiments, tissue preparation methods for single-cell analysis should also be optimized to ensure that samples are of the highest quality and are truly representative of tissues in situ. Here we provide optimized step-by-step protocols for full spectrum flow cytometry panel design, tissue digestion and panel optimization to facilitate the analysis of challenging tissue types.

Molecular cytometry for comprehensive immune profiling.

Molecular cytometry refers to a group of high-parameter technologies for single-cell analysis that share the following traits: (1) combined (multimodal) measurement of protein and transcripts, (2) medium throughput (10-100K cells), and (3) the use of oligonucleotide-tagged antibodies to detect protein expression. The platform can measure over 100 proteins and either hundreds of targeted genes or the whole transcriptome, on a cell-by-cell basis. It is currently one of the most powerful technologies available for immune monitoring. Here, we describe the technology platform (which includes CITE-Seq, REAP-Seq, and AbSeq), provide guidance for its optimization, and discuss advantages and limitations. Finally, we provide some vignettes from studies that demonstrate the application and potential insight that can be gained from molecular cytometry studies.

Single-cell multi-parametric characterization of microbiota by flow cytometry.

It has become evident, that the microbes colonizing the human body have a great impact on health and disease. Investigations of microbiota currently primarily rely on culturomics, high-throughput sequencing and metaproteomics which have considerably advanced our knowledge regarding the role of the microbiota in our environment and for our health. While single-cell phenotyping of immune cells and other somatic cells by flow cytometry has become widely used, the detailed analysis of bacterial cells such as the human microbiota on the single-cell level, is lagging behind. Here, we outline a protocol for the single-cell characterization of bacterial cells from complex microbiota samples, such as stool, by multi-parametric flow cytometry. Our protocol describes the isotype-specific detection of host-antibody coating of intestinal bacteria ex vivo, which together with quantitative DNA staining and light scatter detection comprise an individual's microbiota fingerprint. Cryoconservation and appropriate staining controls ensure reliable, reproducible data generation and analysis. We have automated the analysis of the multi-dimensional data using a segmentation approach by self-organizing map (SOM) algorithm for downstream comparative analyses. Our protocol can be adapted to integrate further phenotypic markers and uses the power of analytical cytometry for the characterization of bacteria on the single-cell level.

Correlative cryo-microscopy pipelines for in situ cellular studies.

Correlative cryo-microscopy pipelines combining light and electron microscopy and tomography in cryogenic conditions (cryoCLEM) on the same sample are powerful methods for investigating the structure of specific cellular targets identified by a fluorescent tag within their unperturbed cellular environment. CryoCLEM approaches circumvent one of the inherent limitations of cryo EM, and specifically cryo electron tomography (cryoET), of identifying the imaged structures in the crowded 3D environment of cells. Whereas several cryoCLEM approaches are based on thinning the sample by cryo FIB milling, here we present detailed protocols of two alternative cryoCLEM approaches for in situ studies of adherent cells at the single-cell level without the need for such cryo-thinning. The first approach is a complete cryogenic pipeline in which both fluorescence and electronic imaging are performed on frozen-hydrated samples, the second is a hybrid cryoCLEM approach in which fluorescence imaging is performed at room temperature, followed by rapid freezing and subsequent cryoEM imaging. We provide a detailed description of the two methods we have employed for imaging fluorescently labeled cellular structures with thickness below 350-500nm, such as cell protrusions and organelles located in the peripheral areas of the cells.

A Pipeline for Dynamic Analysis of Mitochondrial Content in Developing T Cells: Bridging the Gap Between High-Throughput Flow Cytometry and Single-Cell Microscopy Analysis.

Analyzing the dynamics of mitochondrial content in developing T cells is crucial for understanding the metabolic state during T cell development. However, monitoring mitochondrial content in real-time needs a balance of cell viability and image resolution. In this chapter, we present experimental protocols for measuring mitochondrial content in developing T cells using three modalities: bulk analysis via flow cytometry, volumetric imaging in laser scanning confocal microscopy, and dynamic live-cell monitoring in spinning disc confocal microscopy. Next, we provide an image segmentation and centroid tracking-based analysis pipeline for automated quantification of a large number of microscopy images. These protocols together offer comprehensive approaches to investigate mitochondrial dynamics in developing T cells, enabling a deeper understanding of their metabolic processes.

Integrating machine learning algorithms and single-cell analysis to identify gut microbiota-related macrophage biomarkers in atherosclerotic plaques.

Objective: The relationship between macrophages and the gut microbiota in patients with atherosclerosis remains poorly defined, and effective biological markers are lacking. This study aims to elucidate the interplay between gut microbial communities and macrophages, and to identify biomarkers associated with the destabilization of atherosclerotic plaques. The goal is to enhance our understanding of the underlying molecular pathways and to pave new avenues for diagnostic approaches and therapeutic strategies in the disease. Methods: This study employed Weighted Gene Co-expression Network Analysis (WGCNA) and differential expression analysis on atherosclerosis datasets to identify macrophage-associated genes and quantify the correlation between these genes and gut microbiota gene sets. The Random Forest algorithm was utilized to pinpoint PLEK, IRF8, BTK, CCR1, and CD68 as gut microbiota-related macrophage genes, and a nomogram was constructed. Based on the top five genes, a Non-negative Matrix Factorization (NMF) algorithm was applied to construct gut microbiota-related macrophage clusters and analyze their potential biological alterations. Subsequent single-cell analyses were conducted to observe the expression patterns of the top five genes and the interactions between immune cells. Finally, the expression profiles of key molecules were validated using clinical samples from atherosclerosis patients. Results: Utilizing the Random Forest algorithm, we ultimately identified PLEK, IRF8, CD68, CCR1, and BTK as gut microbiota-associated macrophage genes that are upregulated in atherosclerotic plaques. A nomogram based on the expression of these five genes was constructed for use as an auxiliary tool in clinical diagnosis. Single-cell analysis confirmed the specific expression of gut microbiota-associated macrophage genes in macrophages. Clinical samples substantiated the high expression of PLEK in unstable atherosclerotic plaques. Conclusion: Gut microbiota-associated macrophage genes (PLEK, IRF8, CD68, CCR1, and BTK) may be implicated in the pathogenesis of atherosclerotic plaques and could serve as diagnostic markers to aid patients with atherosclerosis.

Diagnostic leukapheresis reveals distinct phenotypes of NSCLC circulating tumor cells.

BACKGROUND: Circulating tumor cells (CTCs) hold immense promise for unraveling tumor heterogeneity and understanding treatment resistance. However, conventional methods, especially in cancers like non-small cell lung cancer (NSCLC), often yield low CTC numbers, hindering comprehensive analyses. This study addresses this limitation by employing diagnostic leukapheresis (DLA) to cancer patients, enabling the screening of larger blood volumes. To leverage DLA's full potential, this study introduces a novel approach for CTC enrichment from DLAs. METHODS: DLA was applied to six advanced stage NSCLC patients. For an unbiased CTC enrichment, a two-step approach based on negative depletion of hematopoietic cells was used. Single-cell (sc) whole-transcriptome sequencing was performed, and CTCs were identified based on gene signatures and inferred copy number variations. RESULTS: Remarkably, this innovative approach led to the identification of unprecedented 3,363 CTC transcriptomes. The extensive heterogeneity among CTCs was unveiled, highlighting distinct phenotypes related to the epithelial-mesenchymal transition (EMT) axis, stemness, immune responsiveness, and metabolism. Comparison with sc transcriptomes from primary NSCLC cells revealed that CTCs encapsulate the heterogeneity of their primary counterparts while maintaining unique CTC-specific phenotypes. CONCLUSIONS: In conclusion, this study pioneers a transformative method for enriching CTCs from DLA, resulting in a substantial increase in CTC numbers. This allowed the creation of the first-ever single-cell whole transcriptome in-depth characterization of the heterogeneity of over 3,300 NSCLC-CTCs. The findings not only confirm the diagnostic value of CTCs in monitoring tumor heterogeneity but also propose a CTC-specific signature that can be exploited for targeted CTC-directed therapies in the future. This comprehensive approach signifies a major leap forward, positioning CTCs as a key player in advancing our understanding of cancer dynamics and paving the way for tailored therapeutic interventions.

Spectral flow cytometry: Fundamentals and future impact.

Spectral flow cytometry has emerged as a significant player in the cytometry marketplace, with the potential for rapid growth. Despite a slow start, the technology has made significant strides in advancing various areas of single-cell analysis utilized by the scientific community. The integration of spectral cytometry into clinical laboratories and diagnostic processes is currently underway and is expected to garner a significant level of widespread acceptance in the near future. However, incorporating a new methodological approach into existing research programs can lead to misunderstandings or even misuse. This chapter offers an introductory yet comprehensive explanation of the scientific principles that form the foundation of spectral cytometry. Specifically, it delves into the unmixing processes that are utilized for data analysis. This overview is designed for those who are new to the field and seeking an informative guide to this exciting emerging technology.