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.

Metabolic adaptation pilots the differentiation of human hematopoietic cells.

A continuous supply of energy is an essential prerequisite for survival and represents the highest priority for the cell. We hypothesize that cell differentiation is a process of optimization of energy flow in a changing environment through phenotypic adaptation. The mechanistic basis of this hypothesis is provided by the established link between core energy metabolism and epigenetic covalent modifications of chromatin. This theory predicts that early metabolic perturbations impact subsequent differentiation. To test this, we induced transient metabolic perturbations in undifferentiated human hematopoietic cells using pharmacological inhibitors targeting key metabolic reactions. We recorded changes in chromatin structure and gene expression, as well as phenotypic alterations by single-cell ATAC and RNA sequencing, time-lapse microscopy, and flow cytometry. Our observations suggest that these metabolic perturbations are shortly followed by alterations in chromatin structure, leading to changes in gene expression. We also show that these transient fluctuations alter the differentiation potential of the cells.

Single cell RNA analysis uncovers the cell differentiation and functionalization for air breathing of frog lung.

The evolution and development of vertebrate lungs have been widely studied due to their significance in terrestrial adaptation. Amphibians possess the most primitive lungs among tetrapods, underscoring their evolutionary importance in bridging the transition from aquatic to terrestrial life. However, the intricate process of cell differentiation during amphibian lung development remains poorly understood. Using single-cell RNA sequencing, we identify 13 cell types in the developing lungs of a land-dwelling frog (Microhyla fissipes). We elucidate the differentiation trajectories and mechanisms of mesenchymal cells, identifying five cell fates and their respective driver genes. Using temporal dynamics analyses, we reveal the gene expression switches of epithelial cells, which facilitate air breathing during metamorphosis. Furthermore, by integrating the published data from another amphibian and two terrestrial mammals, we illuminate both conserved and divergent cellular repertoires during the evolution of tetrapod lungs. These findings uncover the frog lung cell differentiation trajectories and functionalization for breathing in air and provide valuable insights into the cell-type evolution of vertebrate lungs.

An innovative single-cell approach for phenotyping and functional genotyping of CAR NK cells.

BACKGROUND: To accelerate the translation of novel immunotherapeutic treatment approaches, the development of analytic methods to assess their efficacy at early in vitro stages is necessary. Using a droplet-based microfluidic platform, we have established a method for multiparameter quantifiable phenotypic and genomic observations of immunotherapies. Chimeric antigen receptor (CAR) natural killer (NK) cells are of increased interest in the current immunotherapy landscape and thus provide an optimal model for evaluating our novel methodology. METHODS: For this approach, NK cells transduced with a CD19 CAR were compared with non-transduced NK cells in their ability to kill a lymphoma cell line. Using our microfluidic platform, we were able to quantify the increase in cytotoxicity and synaptic contact formation of CAR NK cells over non-transduced NK cells. We then optimized our droplet sorter and successfully used it to separate NK cells based on target cell killing to perform transcriptomic analyses. RESULTS: Our data revealed expected improvement in cytotoxicity with the CD19 CAR but more importantly, provided unique insights into the factors involved in the cytotoxic mechanisms of CAR NK cells. This demonstrates a novel, improved system for accelerating the pre-clinical screening of future immunotherapy treatments. CONCLUSIONS: This study provides a new potential approach for enhanced early screening of immunotherapies to improve their development, with a highly relevant cell model to demonstrate. Additionally, our validation studies provided some potential insights into transcriptomic determinants influencing CAR NK cytotoxicity.

A computational tool suite to facilitate single-cell lineage tracing analyses.

Tracking the lineage relationships of cell populations is of increasing interest in diverse biological contexts. In this issue of Cell Reports Methods, Holze et al. present a suite of computational tools to facilitate such analyses and encourage their broader application.

Advancing skeletal health and disease research with single-cell RNA sequencing.

Orthopedic conditions have emerged as global health concerns, impacting approximately 1.7 billion individuals worldwide. However, the limited understanding of the underlying pathological processes at the cellular and molecular level has hindered the development of comprehensive treatment options for these disorders. The advent of single-cell RNA sequencing (scRNA-seq) technology has revolutionized biomedical research by enabling detailed examination of cellular and molecular diversity. Nevertheless, investigating mechanisms at the single-cell level in highly mineralized skeletal tissue poses technical challenges. In this comprehensive review, we present a streamlined approach to obtaining high-quality single cells from skeletal tissue and provide an overview of existing scRNA-seq technologies employed in skeletal studies along with practical bioinformatic analysis pipelines. By utilizing these methodologies, crucial insights into the developmental dynamics, maintenance of homeostasis, and pathological processes involved in spine, joint, bone, muscle, and tendon disorders have been uncovered. Specifically focusing on the joint diseases of degenerative disc disease, osteoarthritis, and rheumatoid arthritis using scRNA-seq has provided novel insights and a more nuanced comprehension. These findings have paved the way for discovering novel therapeutic targets that offer potential benefits to patients suffering from diverse skeletal disorders.

CD4+ T cell heterogeneity in gestational age and preeclampsia using single-cell RNA sequencing.

A balance between pro-inflammatory decidual CD4+ T cells and FOXP3+ regulatory T cells (FOXP3+ Tregs) is important for maintaining fetomaternal tolerance. Using single-cell RNA-sequencing and T cell receptor repertoire analysis, we determined that diversity and clonality of decidual CD4+ T cell subsets depend on gestational age. Th1/Th2 intermediate and Th1 subsets of CD4+ T cells were clonally expanded in both early and late gestation, whereas FOXP3+ Tregs were clonally expanded in late gestation. Th1/Th2 intermediate and FOXP3+ Treg subsets showed altered gene expression in preeclampsia (PE) compared to healthy late gestation. The Th1/Th2 intermediate subset exhibited elevated levels of cytotoxicity-related gene expression in PE. Moreover, increased Treg exhaustion was observed in the PE group, and FOXP3+ Treg subcluster analysis revealed that the effector Treg like subset drove the Treg exhaustion signatures in PE. The Th1/Th2 intermediate and effector Treg like subsets are possible inflammation-driving subsets in PE.

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.

Unravelling B cell heterogeneity: insights into flow cytometry-gated B cells from single-cell multi-omics data.

Introduction: B cells play a pivotal role in adaptive immunity which has been extensively characterised primarily via flow cytometry-based gating strategies. This study addresses the discrepancies between flow cytometry-defined B cell subsets and their high-confidence molecular signatures using single-cell multi-omics approaches. Methods: By analysing multi-omics single-cell data from healthy individuals and patients across diseases, we characterised the level and nature of cellular contamination within standard flow cytometric-based gating, resolved some of the ambiguities in the literature surrounding unconventional B cell subsets, and demonstrated the variable effects of flow cytometric-based gating cellular heterogeneity across diseases. Results: We showed that flow cytometric-defined B cell populations are heterogenous, and the composition varies significantly between disease states thus affecting the implications of functional studies performed on these populations. Importantly, this paper draws caution on findings about B cell selection and function of flow cytometric-sorted populations, and their roles in disease. As a solution, we developed a simple tool to identify additional markers that can be used to increase the purity of flow-cytometric gated immune cell populations based on multi-omics data (AlliGateR). Here, we demonstrate that additional non-linear CD20, CD21 and CD24 gating can increase the purity of both naïve and memory populations. Discussion: These findings underscore the need to reconsider B cell subset definitions within the literature and propose leveraging single-cell multi-omics data for refined characterisation. We show that single-cell multi-omics technologies represent a powerful tool to bridge the gap between surface marker-based annotations and the intricate molecular characteristics of B cell subsets.

Learning Consistency and Specificity of Cells From Single-Cell Multi-Omic Data.

Advancements in single-cell technologies concomitantly develop the epigenomic and transcriptomic profiles at the cell levels, providing opportunities to explore the potential biological mechanisms. Even though significant efforts have been dedicated to them, it remains challenging for the integration analysis of multi-omic data of single-cell because of the heterogeneity, complicated coupling and interpretability of data. To handle these issues, we propose a novel self-representation Learning-based Multi-omics data Integrative Clustering algorithm (sLMIC) for the integration of single-cell epigenomic profiles (DNA methylation or scATAC-seq) and transcriptomic (scRNA-seq), which the consistent and specific features of cells are explicitly extracted facilitating the cell clustering. Specifically, sLMIC constructs a graph for each type of single-cell data, thereby transforming omics data into multi-layer networks, which effectively removes heterogeneity of omic data. Then, sLMIC employs the low-rank and exclusivity constraints to separate the self-representation of cells into two parts, i.e., the shared and specific features, which explicitly characterize the consistency and diversity of omic data, providing an effective strategy to model the structure of cell types. Feature extraction and cell clustering are jointly formulated as an overall objective function, where latent features of data are obtained under the guidance of cell clustering. The extensive experimental results on 13 multi-omics datasets of single-cell from diverse organisms and tissues indicate that sLMIC observably exceeds the advanced algorithms regarding various measurements.

Screening and validation of atherosclerosis PAN-apoptotic immune-related genes based on single-cell sequencing.

Background: Carotid atherosclerosis (CAS) is a complication of atherosclerosis (AS). PAN-optosome is an inflammatory programmed cell death pathway event regulated by the PAN-optosome complex. CAS's PAN-optosome-related genes (PORGs) have yet to be studied. Hence, screening the PAN-optosome-related diagnostic genes for treating CAS was vital. Methods: We introduced transcriptome data to screen out differentially expressed genes (DEGs) in CAS. Subsequently, WGCNA analysis was utilized to mine module genes about PANoptosis score. We performed differential expression analysis (CAS samples vs. standard samples) to obtain CAS-related differentially expressed genes at the single-cell level. Venn diagram was executed to identify PAN-optosome-related differential genes (POR-DEGs) associated with CAS. Further, LASSO regression and RF algorithm were implemented to were executed to build a diagnostic model. We additionally performed immune infiltration and gene set enrichment analysis (GSEA) based on diagnostic genes. We verified the accuracy of the model genes by single-cell nuclear sequencing and RT-qPCR validation of clinical samples, as well as in vitro cellular experiments. Results: We identified 785 DEGs associated with CAS. Then, 4296 module genes about PANoptosis score were obtained. We obtained the 7365 and 1631 CAS-related DEGs at the single-cell level, respectively. 67 POR-DEGs were retained Venn diagram. Subsequently, 4 PAN-optosome-related diagnostic genes (CNTN4, FILIP1, PHGDH, and TFPI2) were identified via machine learning. Cellular function tests on four genes showed that these genes have essential roles in maintaining arterial cell viability and resisting cellular senescence. Conclusion: We obtained four PANoptosis-related diagnostic genes (CNTN4, FILIP1, PHGDH, and TFPI2) associated with CAS, laying a theoretical foundation for treating CAS.

Integrating single cell expression quantitative trait loci summary statistics to understand complex trait risk genes.

Transcriptome-wide association study (TWAS) is a popular approach to dissect the functional consequence of disease associated non-coding variants. Most existing TWAS use bulk tissues and may not have the resolution to reveal cell-type specific target genes. Single-cell expression quantitative trait loci (sc-eQTL) datasets are emerging. The largest bulk- and sc-eQTL datasets are most conveniently available as summary statistics, but have not been broadly utilized in TWAS. Here, we present a new method EXPRESSO (EXpression PREdiction with Summary Statistics Only), to analyze sc-eQTL summary statistics, which also integrates 3D genomic data and epigenomic annotation to prioritize causal variants. EXPRESSO substantially improves existing methods. We apply EXPRESSO to analyze multi-ancestry GWAS datasets for 14 autoimmune diseases. EXPRESSO uniquely identifies 958 novel gene x trait associations, which is 26% more than the second-best method. Among them, 492 are unique to cell type level analysis and missed by TWAS using whole blood. We also develop a cell type aware drug repurposing pipeline, which leverages EXPRESSO results to identify drug compounds that can reverse disease gene expressions in relevant cell types. Our results point to multiple drugs with therapeutic potentials, including metformin for type 1 diabetes, and vitamin K for ulcerative colitis.

Integrating population genetics, stem cell biology and cellular genomics to study complex human diseases.

Human pluripotent stem (hPS) cells can, in theory, be differentiated into any cell type, making them a powerful in vitro model for human biology. Recent technological advances have facilitated large-scale hPS cell studies that allow investigation of the genetic regulation of molecular phenotypes and their contribution to high-order phenotypes such as human disease. Integrating hPS cells with single-cell sequencing makes identifying context-dependent genetic effects during cell development or upon experimental manipulation possible. Here we discuss how the intersection of stem cell biology, population genetics and cellular genomics can help resolve the functional consequences of human genetic variation. We examine the critical challenges of integrating these fields and approaches to scaling them cost-effectively and practically. We highlight two areas of human biology that can particularly benefit from population-scale hPS cell studies, elucidating mechanisms underlying complex disease risk loci and evaluating relationships between common genetic variation and pharmacotherapeutic phenotypes.

Single-cell mtDNA dynamics in tumors is driven by coregulation of nuclear and mitochondrial genomes.

The extent of cell-to-cell variation in tumor mitochondrial DNA (mtDNA) copy number and genotype, and the phenotypic and evolutionary consequences of such variation, are poorly characterized. Here we use amplification-free single-cell whole-genome sequencing (Direct Library Prep (DLP+)) to simultaneously assay mtDNA copy number and nuclear DNA (nuDNA) in 72,275 single cells derived from immortalized cell lines, patient-derived xenografts and primary human tumors. Cells typically contained thousands of mtDNA copies, but variation in mtDNA copy number was extensive and strongly associated with cell size. Pervasive whole-genome doubling events in nuDNA associated with stoichiometrically balanced adaptations in mtDNA copy number, implying that mtDNA-to-nuDNA ratio, rather than mtDNA copy number itself, mediated downstream phenotypes. Finally, multimodal analysis of DLP+ and single-cell RNA sequencing identified both somatic loss-of-function and germline noncoding variants in mtDNA linked to heteroplasmy-dependent changes in mtDNA copy number and mitochondrial transcription, revealing phenotypic adaptations to disrupted nuclear/mitochondrial balance.

Single-cell and spatial transcriptomics analysis of human adrenal aging.

OBJECTIVE: The human adrenal cortex comprises three functionally and structurally distinct layers that produce layer-specific steroid hormones. With aging, the human adrenal cortex undergoes functional and structural alteration or "adrenal aging", leading to the unbalanced production of steroid hormones. Given the marked species differences in adrenal biology, the underlying mechanisms of human adrenal aging have not been sufficiently studied. This study was designed to elucidate the mechanisms linking the functional and structural alterations of the human adrenal cortex. METHODS: We conducted single-cell RNA sequencing and spatial transcriptomics analysis of the aged human adrenal cortex. RESULTS: The data of this study suggest that the layer-specific alterations of multiple signaling pathways underlie the abnormal layered structure and layer-specific changes in steroidogenic cells. We also highlighted that macrophages mediate age-related adrenocortical cell inflammation and senescence. CONCLUSIONS: This study is the first detailed analysis of the aged human adrenal cortex at single-cell resolution and helps to elucidate the mechanism of human adrenal aging, thereby leading to a better understanding of the pathophysiology of age-related disorders associated with adrenal aging.

Pancreatic ß-Cell Identity Change through the Lens of Single-Cell Omics Research.

The main hallmark in the development of both type 1 and type 2 diabetes is a decline in functional ß-cell mass. This decline is predominantly attributed to ß-cell death, although recent findings suggest that the loss of ß-cell identity may also contribute to ß-cell dysfunction. This phenomenon is characterized by a reduced expression of key markers associated with ß-cell identity. This review delves into the insights gained from single-cell omics research specifically focused on ß-cell identity. It highlights how single-cell omics based studies have uncovered an unexpected level of heterogeneity among ß-cells and have facilitated the identification of distinct ß-cell subpopulations through the discovery of cell surface markers, transcriptional regulators, the upregulation of stress-related genes, and alterations in chromatin activity. Furthermore, specific subsets of ß-cells have been identified in diabetes, such as displaying an immature, dedifferentiated gene signature, expressing significantly lower insulin mRNA levels, and expressing increased ß-cell precursor markers. Additionally, single-cell omics has increased insight into the detrimental effects of diabetes-associated conditions, including endoplasmic reticulum stress, oxidative stress, and inflammation, on ß-cell identity. Lastly, this review outlines the factors that may influence the identification of ß-cell subpopulations when designing and performing a single-cell omics experiment.

Clinical and CSF single-cell profiling of post-COVID-19 cognitive impairment.

Natural history and mechanisms for persistent cognitive symptoms ("brain fog") following acute and often mild COVID-19 are unknown. In a large prospective cohort of people who underwent testing a median of 9 months after acute COVID-19 in the New York City/New Jersey area, we found that cognitive dysfunction is common; is not influenced by mood, fatigue, or sleepiness; and is correlated with MRI changes in very few people. In a subgroup that underwent cerebrospinal fluid analysis, there are no changes related to Alzheimer's disease or neurodegeneration. Single-cell gene expression analysis in the cerebrospinal fluid shows findings consistent with monocyte recruitment, chemokine signaling, cellular stress, and suppressed interferon response-especially in myeloid cells. Longitudinal analysis shows slow recovery accompanied by key alterations in inflammatory genes and increased protein levels of CXCL8, CCL3L1, and sTREM2. These findings suggest that the prognosis for brain fog following COVID-19 correlates with myeloid-related chemokine and interferon-responsive genes.

Reference-free inferring of transcriptomic events in cancer cells on single-cell data.

BACKGROUND: Cancerous cells' identity is determined via a mixture of multiple factors such as genomic variations, epigenetics, and the regulatory variations that are involved in transcription. The differences in transcriptome expression as well as abnormal structures in peptides determine phenotypical differences. Thus, bulk RNA-seq and more recent single-cell RNA-seq data (scRNA-seq) are important to identify pathogenic differences. In this case, we rely on k-mer decomposition of sequences to identify pathogenic variations in detail which does not need a reference, so it outperforms more traditional Next-Generation Sequencing (NGS) analysis techniques depending on the alignment of the sequences to a reference. RESULTS: Via our alignment-free analysis, over esophageal and glioblastoma cancer patients, high-frequency variations over multiple different locations (repeats, intergenic regions, exons, introns) as well as multiple different forms (fusion, polyadenylation, splicing, etc.) could be discovered. Additionally, we have analyzed the importance of less-focused events systematically in a classic transcriptome analysis pipeline where these events are considered as indicators for tumor prognosis, tumor prediction, tumor neoantigen inference, as well as their connection with respect to the immune microenvironment. CONCLUSIONS: Our results suggest that esophageal cancer (ESCA) and glioblastoma processes can be explained via pathogenic microbial RNA, repeated sequences, novel splicing variants, and long intergenic non-coding RNAs (lincRNAs). We expect our application of reference-free process and analysis to be helpful in tumor and normal samples differential scRNA-seq analysis, which in turn offers a more comprehensive scheme for major cancer-associated events.

Error modelled gene expression analysis (EMOGEA) provides a superior overview of time course RNA-seq measurements and low count gene expression.

Temporal RNA-sequencing (RNA-seq) studies of bulk samples provide an opportunity for improved understanding of gene regulation during dynamic phenomena such as development, tumor progression or response to an incremental dose of a pharmacotherapeutic. Moreover, single-cell RNA-seq (scRNA-seq) data implicitly exhibit temporal characteristics because gene expression values recapitulate dynamic processes such as cellular transitions. Unfortunately, temporal RNA-seq data continue to be analyzed by methods that ignore this ordinal structure and yield results that are often difficult to interpret. Here, we present Error Modelled Gene Expression Analysis (EMOGEA), a framework for analyzing RNA-seq data that incorporates measurement uncertainty, while introducing a special formulation for those acquired to monitor dynamic phenomena. This method is specifically suited for RNA-seq studies in which low-count transcripts with small-fold changes lead to significant biological effects. Such transcripts include genes involved in signaling and non-coding RNAs that inherently exhibit low levels of expression. Using simulation studies, we show that this framework down-weights samples that exhibit extreme responses such as batch effects allowing them to be modeled with the rest of the samples and maintain the degrees of freedom originally envisioned for a study. Using temporal experimental data, we demonstrate the framework by extracting a cascade of gene expression waves from a well-designed RNA-seq study of zebrafish embryogenesis and an scRNA-seq study of mouse pre-implantation and provide unique biological insights into the regulation of genes in each wave. For non-ordinal measurements, we show that EMOGEA has a much higher rate of true positive calls and a vanishingly small rate of false negative discoveries compared to common approaches. Finally, we provide two packages in Python and R that are self-contained and easy to use, including test data.