Single-cell transcriptomics to explore the immune system in health and disease

Dissection of the global responses of mandarin fish pyloric cecum to an acute ranavirus (MRV) infection reveals the formation of serositis and then ascites

Mandarin fish ranavirus (MRV), a new member of the species Ranavirus micropterus1, sharing over 98% whole-genome nucleotide identity with the well-known largemouth bass virus (LMBV), is a distinct member of the genus Ranavirus within the family Iridoviridae. Our recent work showed that acute MRV infection predominantly affects the pyloric cecum, a critical visceral organ in mandarin fish, and was hypothesized to drive the characteristic external clinical sign of severe ascites. In this study, we reveal that acute MRV infection initially targets the serosal layer of the pyloric cecum of mandarin fish, leading to rapid progression into fibrinous serositis characterized by serosal hypertrophy, fibrosis, hyperemia, edema, and tissue adhesions. Using single-cell RNA sequencing, we dissect the cellular composition of epithelial, immune, and stromal populations, identifying significant enrichment of macrophages and granulocytes, alongside T and natural killer cells, as key mediators of acute cytokine and inflammatory responses. Then, robust experimental evidence demonstrates that MRV infects specific immune cell subsets of T and B cells and stromal cells of fibroblasts, myofibroblasts, endothelial cells, and pericytes, resulting in upregulation of genes and pathways associated with extracellular matrix (ECM) formation, collagen biosynthesis, and vascular remodeling in the hyperplastic serosal zone. Additionally, both host-derived type V collagens and MRV-encoded collagens are implicated in ECM formation in the hypertrophic serosa. Collectively, this study provides a comprehensive single-cell resolution analysis of the pyloric cecum's response to acute MRV infection and highlights virus-driven serositis as the underlying cause of severe ascites in mandarin fish.IMPORTANCEThe pyloric cecum is a vital digestive and immune organ in many bony fish species, including the mandarin fish, a carnivorous species with an exceptionally developed pyloric cecum comprising 207-326 ceca per individual. While MRV/LMBV infects various fish species, severe ascites is uniquely observed in infected mandarin fish. This study demonstrates that acute MRV infection induces fibrinous serositis in the pyloric cecum, characterized by hyperemia, edema, and hyperplasia, ultimately resulting in ascites and mortality. Leveraging single-cell RNA sequencing, we provide a detailed landscape of the cell types affected or involved in the inflammatory response, revealing their roles in the pathogenesis of serositis. These findings advance our understanding of MRV-induced pathology and its species-specific manifestations.

Advances and applications in single-cell and spatial genomics

The applications of single-cell and spatial technologies in recent times have revolutionized the present understanding of cellular states and the cellular heterogeneity inherent in complex biological systems. These advancements offer unprecedented resolution in the examination of the functional genomics of individual cells and their spatial context within tissues. In this review, we have comprehensively discussed the historical development and recent progress in the field of single-cell and spatial genomics. We have reviewed the breakthroughs in single-cell multi-omics technologies, spatial genomics methods, and the computational strategies employed toward the analyses of single-cell atlas data. Furthermore, we have highlighted the advances made in constructing cellular atlases and their clinical applications, particularly in the context of disease. Finally, we have discussed the emerging trends, challenges, and opportunities in this rapidly evolving field.

Inflammatory Cell Interactions in the Rotator Cuff Microenvironment: Insights From Single-Cell Sequencing

Rotator cuff injuries are a common cause of shoulder pain and dysfunction, with chronic inflammation complicating recovery. Recent advances in single-cell RNA sequencing (scRNA-seq) have provided new insights into the immune cell interactions within the rotator cuff microenvironment during injury and healing. This review focuses on the application of scRNA-seq to explore the roles of immune and nonimmune cells, including macrophages, T-cells, fibroblasts, and myofibroblasts, in driving inflammation, tissue repair, and fibrosis. We discuss how immune cell crosstalk and interactions with the extracellular matrix influence the progression of healing or pathology. Single-cell analyses have identified distinct molecular signatures associated with chronic inflammation, which may contribute to persistent tissue damage. Additionally, we highlight the therapeutic potential of targeting inflammation in rotator cuff repair, emphasizing personalized medicine approaches. Overall, the integration of scRNA-seq in studying rotator cuff injuries enhances our understanding of the cellular mechanisms involved and offers new perspectives for developing targeted treatments in regenerative medicine.

Spatial genomics: Mapping the landscape of fibrosis

Organ fibrosis causes major morbidity and mortality worldwide. Treatments for fibrosis are limited, with organ transplantation being the only cure. Here, we review how various state-of-the-art spatial genomics approaches are being deployed to interrogate fibrosis across multiple organs, providing exciting insights into fibrotic disease pathogenesis. These include the detailed topographical annotation of pathogenic cell populations and states, detection of transcriptomic perturbations in morphologically normal tissue, characterization of fibrotic and homeostatic niches and their cellular constituents, and in situ interrogation of ligand-receptor interactions within these microenvironments. Together, these powerful readouts enable detailed analysis of fibrosis evolution across time and space.

CD177+ neutrophils exacerbate septic lung injury via the NETs/AIM2 pathway: An experimental and bioinformatics study

BACKGROUND

Acute lung injury (ALI) is one of the most common complications of sepsis. However, the underlying mechanisms and effective treatment strategies remain poorly understood. Immune cells are crucial in sepsis-induced lung injury, yet the heterogeneity of the immune cell populations involved in this context is not well characterized.

METHODS

This study established a Cecal Ligation and Puncture (CLP) mouse model and employed single-cell sequencing along with molecular biology experimental methods to identify the primary functional subgroups of immune cells associated with sepsis-induced ALI, thereby elucidating the key mechanisms related to sepsis-induced ALI.

RESULTS

Our analysis revealed that, in comparison to normal mice, the top 100 differentially expressed genes (DEGs) in septic lung tissue during the acute phase predominantly originate from neutrophils. Cd177 antigen (Cd177)+ neutrophils represent the predominant subpopulation of neutrophils in septic lung tissue. These cells exhibit unique pro-inflammatory and oxidative stress characteristics, and they are capable of producing excessive neutrophil extracellular traps (NETs). NETs can aggravate ALI by activating Absent in Melanoma 2 (AIM2) inflammasome. Furthermore, we discovered that melatonin could effectively inhibit the infiltration of Cd177+ neutrophils in septic lung tissue, reduce the expression levels of NETs, and diminish the activation of AIM2, thereby improving lung injury.

CONCLUSION

Our research provides novel insights and potential therapeutic targets for the treatment of sepsis-induced ALI.

Single-Cell Analysis Reveals Tissue-Specific T Cell Adaptation and Clonal Distribution Across the Human Gut-Liver-Blood Axis

Understanding T cell clonal relationships and tissue-specific adaptations is crucial for deciphering human immune responses, particularly within the gut-liver axis. We performed paired single-cell RNA and T cell receptor sequencing on matched colon (epithelium, lamina propria), liver, and blood T cells from the same human donors. This approach tracked clones across sites and assessed microenvironmental impacts on T cell phenotype. While some clones were shared between blood and tissues, colonic intraepithelial lymphocytes (IELs) exhibited limited overlap with lamina propria T cells, suggesting a largely resident population. Furthermore, tissue-resident memory T cells (TRM) in the colon and liver displayed distinct transcriptional profiles. Notably, our analysis suggested that factors enriched in the liver microenvironment may influence the phenotype of colon lamina propria TRM. This integrated single-cell analysis maps T cell clonal distribution and adaptation across the gut-liver-blood axis, highlighting a potential liver role in shaping colonic immunity.

Sea urchin immune cells and associated microbiota co-exposed to iron oxide nanoparticles activate cellular and molecular reprogramming that promotes physiological adaptation

The innate immune system is the first player involved in the recognition/interaction with nanomaterials. Still, it is not the only system involved. The co-evolution of the microbiota with the innate immune system built an interdependence regulating immune homeostasis that is poorly studied. Herein, the simultaneous interaction of iron-oxide nanoparticles (Fe-oxide NPs), immune cells, and the microbiota associated with the blood of the sea urchin Paracentrotus lividus was explored by using a microbiota/immune cell model in vitro-ex vivo and a battery of complementary tools, including Raman spectroscopy, 16S Next-Generation Sequencing, high-content imaging, NanoString nCounter. Our findings highlight the P. lividus immune cells and microbiota dynamics in response to Fe-oxide NPs, including i) morphological rearrangement and immune cell health status maintenance (intracellular trafficking increasing, no phenotypic alterations or caspase 3/7 activation), ii) transcriptomic reprogramming in immune cells (Smad6, Lmo2, Univin, suPaxB, Frizzled-7, Fgfr2, Gp96 upregulation), iii) immune signaling unchanged (e.g., P-p38 MAPK, P-ERK, TLR4, IL-6 protein level unchanged), iv) enrichment in extracellular vesicle released in the co-culture medium, and v) a shift in the composition of microbial groups mainly in favor of Gram-positive bacteria (e.g., Firmicutes, Actinobacteria),. Our findings suggest that Fe-oxide NPs induce a multi-level immune cell-microbiota response restoring homeostasis.

Systematic reconstruction of molecular pathway signatures using scalable single-cell perturbation screens

Recent advancements in functional genomics have provided an unprecedented ability to measure diverse molecular modalities, but predicting causal regulatory relationships from observational data remains challenging. Here, we leverage pooled genetic screens and single-cell sequencing (Perturb-seq) to systematically identify the targets of signalling regulators in diverse biological contexts. We demonstrate how Perturb-seq is compatible with recent and commercially available advances in combinatorial indexing and next-generation sequencing, and perform more than 1,500 perturbations split across six cell lines and five biological signalling contexts. We introduce an improved computational framework (Mixscale) to address cellular variation in perturbation efficiency, alongside optimized statistical methods to learn differentially expressed gene lists and conserved molecular signatures. Finally, we demonstrate how our Perturb-seq derived gene lists can be used to precisely infer changes in signalling pathway activation for in vivo and in situ samples. Our work enhances our understanding of signalling regulators and their targets, and lays a computational framework towards the data-driven inference of an 'atlas' of perturbation signatures.

Antigen-driven T cell responses in rheumatic diseases: insights from T cell receptor repertoire studies

Advances in T cell receptor (TCR) profiling techniques have substantially improved our ability to investigate T cell responses to antigens that are presented on HLA class I and class II molecules and associations between autoimmune T cells and rheumatic diseases. Early-stage studies in axial spondyloarthritis (axSpA) identified disease-associated T cell clonotypes, benefiting from the relative genetic homogeneity of the disease. However, both the genetic and the T cell immunological landscape are more complex in other rheumatic diseases. The diversity or redundancy in the TCR repertoire, epitope spreading over disease duration, genetic heterogeneity of HLA genes or other loci, and the diversity of epitopes contributing to disease pathogenesis and persistent inflammation are all likely to contribute to this complexity. TCR profiling holds promise for identifying key antigenic drivers and phenotypic T cell states that sustain autoimmunity in rheumatic diseases. Here, we review key findings from TCR repertoire studies in axSpA and other chronic inflammatory rheumatic diseases including psoriatic arthritis, rheumatoid arthritis, systemic lupus erythematosus and Sjögren syndrome. We explore how TCR profiling technologies, if applied to better controlled studies focused on early disease stages and genetically homogeneous subsets, can facilitate disease monitoring and the development of therapeutics targeting autoimmune T cells, their cognate antigens, or their underlying biology.

Transcriptomics in Human Septic Shock: State of the Art

Background: Septic shock is a complex syndrome characterized by signs of intense systemic inflammation and a profound dysregulation of the immune response. Large-scale gene expression analysis is a valuable tool in this scenario because sepsis affects various cellular components and signaling pathways. Results: In this article, we provide an overview of the transcriptomic studies that investigated human sepsis from 2007 to 2024, highlighting their major contributions. Conclusions: The field, however, still faces substantial limitations and several challenges. To advance further, we believe that standardization of sample collection and data analysis, preservation of cell and tissue architecture, and integration with other omics techniques are crucial for a broader understanding of this lethal disease.

CD177, MYBL2, and RRM2 Are Potential Biomarkers for Musculoskeletal Infections

BACKGROUND

Biomarkers of infection are measurable indicators that reflect the presence of an infection in the body. They are particularly valuable for detecting infections and tracking treatment responses. Previous transcriptome analysis of peripheral blood mononuclear cells (PBMCs) collected from patients during the active phase of diabetic foot infection identified the upregulation of several genes, including a neutrophil-specific cell surface glycoprotein, CD177, an Myb-related transcription factor 2 (MYBL2), and ribonucleotide reductase regulatory subunit M2 (RRM2). We aimed to investigate whether these observations in diabetic foot infections could be extrapolated to other musculoskeletal infections.

QUESTIONS/PURPOSES

(1) Are the protein concentrations of CD177, MYBL2, and RRM2 elevated in serum or PBMCs of patients with musculoskeletal infections? (2) Do serum and PBMC concentrations of CD177, MYBL2, and RRM2 decrease in response to antibiotic therapy? (3) Can these biomarkers give diagnostic accuracy and differentiate patients with musculoskeletal infections from controls?

METHODS

From April 2023 to June 2024, we treated 26 patients presenting with clinical symptoms and signs of acute musculoskeletal infections, including elevated inflammatory markers (white blood cell [WBC] and C-reactive protein [CRP]) and local changes such as swelling, erythema, tenderness or pain, warmth, purulent drainage, sinus tract, or wound leading to bone or hardware. Diagnosis included periprosthetic joint infection (PJI), foot and ankle infection (FAI), fracture-related infection (FRI), and septic arthritis of the native joints. Patients with chronic recurrent osteomyelitis, PJI, or FRI were excluded from the study. Among the 26 patients deemed potentially eligible, 19% (5) were excluded for the following reasons: prison inmate (1), unable to provide consent because of severe sepsis (1), mental illness (1), and declined to participate (2). Of the 81% (21) of patients who provided consent, cultures from 9.5% (2) were negative. These two patients were ultimately diagnosed with inflammatory arthritis: gout (1) and rheumatoid arthritis (1); thus, the musculoskeletal infection group for analysis consisted of 73.1% (19 of 26) of patients. A control group of 21 patients undergoing elective foot or ankle deformity correction surgery without infections or systemic inflammation was included. Because foot or ankle deformity is highly unlikely to influence the immunologic profile of the subjects, we believed that these patients would serve as an appropriate control group. Other than the absence of infection and the lower prevalence of diabetes mellitus, the control group was comparable to the study group in terms of demographics and clinical factors, including age and sex distribution. We collected blood samples from both patients and controls and quantified CD177, MYBL2, and RRM2 RNA transcription levels in the PBMC using qRT-PCR. We also assessed protein concentrations in the serum and PBMC using an enzyme-linked immunosorbent assay. A comparative analysis of the three biomarkers was performed on 19 patients with musculoskeletal infections with positive cultures and 21 controls to assess their diagnostic potential using the unpaired nonparametric t-test with the Mann-Whitney test. We obtained 8-week follow-up blood samples from seven patients with musculoskeletal infections who clinically healed. Healing was defined by normalization of inflammatory markers (WBC and CRP) and absence of swelling, erythema, local tenderness or pain, warmth, purulent drainage, sinus tract, or open wound. We performed a comparative analysis of the seven patients during active infection and after treatment to determine a change in the level of CD177, MYBL2, and RRM2 in their serum and PBMCs. These findings were also compared with those of the control group. We evaluated the diagnostic accuracy of CD177, MYBL2, and RRM2 for musculoskeletal infections using receiver operating characteristic (ROC) curve analysis.

RESULTS

The musculoskeletal infections group showed a larger increased serum and PBMC concentrations of CD177, MYBL2, and RRM2 proteins compared with the control group. The mean protein concentrations of CD177, MYBL2, and RRM2 were increased in the serum and PBMC of the musculoskeletal infections group compared with the controls. Serum levels of all biomarkers investigated were higher in musculoskeletal infections group compared with the control group (CD177 227 [155 to 432] versus 54 [10 to 100], difference of medians 173, p < 0.01; MYBL2 255 [231 to 314] versus 180 [148 to 214], difference of medians 75, p < 0.01; RRM2 250 [216 to 305] versus 190 [148 to 255], difference of medians 60, p < 0.01). Similarly, PBMC levels of all biomarkers were higher in the musculoskeletal infections group (CD177 55.3 [39.1 to 80.5] versus 17.5 [10.5 to 27.5], difference of medians 37.8, p < 0.01; MYBL2 144 [114 to 190] versus 91 [70 to 105], difference of medians 53, p < 0.01; RRM2 168 [143 to 202] versus 100 [77.5 to 133], difference of medians 68, p < 0.01). Additionally, serum levels of all biomarkers decreased in seven patients with musculoskeletal infections after infection treatment (CD177 3080 [2690 to 3320] versus 4250 [3100 to 8640], difference of medians 1170, p < 0.01; MYBL2 4340 [4120 to 4750] versus 5010 [4460 to 5880], difference of medians 670, p < 0.01; RRM2 4350 [3980 to 5000] versus 5025 [4430 to 6280], difference of medians 675, p = 0.01). Similarly, PBMC levels of all biomarkers were lower after infection treatment (CD177 805 [680 to 980] versus 1025 [750 to 1610], difference of medians 220, p < 0.01; MYBL2 2300 [2100 to 2550] versus 2680 [2220 to 3400], difference of medians 380, p = 0.02; RRM2 2720 [2500 to 3200] versus 3350 [2825 to 4030], difference of medians 630, p < 0.01). The area under the ROC curve for diagnosing musculoskeletal infections in the serum and PBMC was as follows: CD177 95% confidence interval [CI] > 0.99 and > 0.99, MYBL2 95% CI > 0.99 and > 0.99, and RRM2 95% CI = 0.96 and > 0.99, respectively.

CONCLUSION

We may utilize blood-based tests for CD177, MYBL2, and RRM2 to aid in the diagnosis of musculoskeletal infections, particularly when arthrocentesis or obtaining tissue culture is challenging. They may also assist in monitoring treatment response. As some of these biomarkers may also be elevated in other inflammatory conditions, a large-scale clinical study is needed to confirm their reliability in differentiating musculoskeletal infections from other inflammatory conditions.

CLINICAL RELEVANCE

CD177, MYBL2, and RRM2 proteins in blood samples may serve as novel biomarkers for diagnosing and monitoring treatment response in musculoskeletal infections.

Towards the Next Generation of Data-Driven Therapeutics Using Spatially Resolved Single-Cell Technologies and Generative AI

Recent advances in multi-omics and spatially resolved single-cell technologies have revolutionised our ability to profile millions of cellular states, offering unprecedented opportunities to understand the complex molecular landscapes of human tissues in both health and disease. These developments hold immense potential for precision medicine, particularly in the rational design of novel therapeutics for treating inflammatory and autoimmune diseases. However, the vast, high-dimensional data generated by these technologies present significant analytical challenges, such as distinguishing technical variation from biological variation or defining relevant questions that leverage the added spatial dimension to improve our understanding of tissue organisation. Generative artificial intelligence (AI), specifically variational autoencoder- or transformer-based latent variable models, provides a powerful and flexible approach to addressing these challenges. These models make inferences about a cell's intrinsic state by effectively identifying complex patterns, reducing data dimensionality and modelling the biological variability in single-cell datasets. This review explores the current landscape of single-cell and spatial multi-omics technologies, the application of generative AI in data analysis and modelling and their transformative impact on our understanding of autoimmune diseases. By combining spatial and single-cell data with advanced AI methodologies, we highlight novel insights into the pathogenesis of autoimmune disorders and outline future directions for leveraging these technologies to achieve the goal of AI-powered personalised medicine.

c-JUN interacts with HDAC1 as a potential combinatorial therapeutic target in acute myeloid leukemia

Acute myeloid leukemia (AML) is a biologically heterogeneous disease originating from the clonal expansion of hematopoietic stem cells (HSCs). Clonal expansion of hematopoietic stem cell progenitors (HSC-Prog), along with a block in differentiation, are hallmark features of AML. The disease is characterized by poor clinical outcomes, highlighting the urgent need for effective therapeutic strategies and suitable drug targets. We conducted multi-omics analyses, including single-cell RNA sequencing (scRNA-seq), Mendelian randomization (MR), and bulk RNA-seq, to investigate HDAC1's oncogenic role in AML. We identified specific gene signatures at the single-cell level. MR with eQTL data established causal links, and TCGA-LAML RNA-seq provided prognostic insights. Analysis of cellular communication and transcription factors revealed high c-JUN activity in HSC-Prog. We confirmed the association of c-JUN with HDAC1 through Western blotting and Co-immunoprecipitation (Co-IP). Functional validation of c-JUN in AML cells was performed via flow cytometry in vitro. The effectiveness of drugs targeting c-JUN and HDAC1 was assessed in mouse models using live imaging methods like in vivo imaging system (IVIS) and iSMAART. We identified the activity of c-JUN is specifically enhanced in HSC-Prog in AML patients. We suggest a potential regulatory relationship between c-JUN and HDAC1 in AML tumor cells. Inhibition of c-JUN can suppress cell proliferation and CD33 expression in AML, enhancing susceptibility to natural killer (NK) cell-mediated cytotoxicity. The combination of agents targeting c-JUN (Ailanthone) and HDAC1 (Panobinostat) showed robust efficacy in treating AML in xenograft mouse models, outperforming monotherapy. We also observed that the combination of Ailanthone and Panobinostat therapy displayed a safe pharmacological profile without dose-dependent toxicity, suggesting its potential as a therapeutic strategy.

Simulation of adaptive immune receptors and repertoires with complex immune information to guide the development and benchmarking of AIRR machine learning

Machine learning (ML) has shown great potential in the adaptive immune receptor repertoire (AIRR) field. However, there is a lack of large-scale ground-truth experimental AIRR data suitable for AIRR-ML-based disease diagnostics and therapeutics discovery. Simulated ground-truth AIRR data are required to complement the development and benchmarking of robust and interpretable AIRR-ML methods where experimental data is currently inaccessible or insufficient. The challenge for simulated data to be useful is incorporating key features observed in experimental repertoires. These features, such as antigen or disease-associated immune information, cause AIRR-ML problems to be challenging. Here, we introduce LIgO, a software suite, which simulates AIRR data for the development and benchmarking of AIRR-ML methods. LIgO incorporates different types of immune information both on the receptor and the repertoire level and preserves native-like generation probability distribution. Additionally, LIgO assists users in determining the computational feasibility of their simulations. We show two examples where LIgO supports the development and validation of AIRR-ML methods: (i) how individuals carrying out-of-distribution immune information impacts receptor-level prediction performance and (ii) how immune information co-occurring in the same AIRs impacts the performance of conventional receptor-level encoding and repertoire-level classification approaches. LIgO guides the advancement and assessment of interpretable AIRR-ML methods.

Evaluation of T Cell Receptor Construction Methods from scRNA-Seq Data

T cell receptors (TCRs) serve key roles in the adaptive immune system by enabling recognition and response to pathogens and irregular cells. Various methods have been developed for TCR construction from single-cell RNA sequencing (scRNA-seq) datasets, each with its unique characteristics. Yet, a comprehensive evaluation of their relative performance under different conditions remains elusive. In this study, we conducted a benchmark analysis utilizing experimental single-cell immune profiling datasets. Additionally, we introduced a novel simulator, YASIM-scTCR (Yet Another SIMulator for single-cell TCR), capable of generating scTCR-seq reads containing diverse TCR-derived sequences with different sequencing depths and read lengths. Our results consistently showed that TRUST4 and MiXCR outperformed others across multiple datasets, while DeRR demonstrated considerable accuracy. We also discovered that the sequencing depth inherently imposes a critical constraint on successful TCR construction from scRNA-seq data. In summary, we present a benchmark study to aid researchers in choosing the appropriate method for reconstructing TCRs from scRNA-seq data.

inDrops-2: a flexible, versatile and cost-efficient droplet microfluidic approach for high-throughput scRNA-seq of fresh and preserved clinical samples

The expansion of single-cell analytical techniques has empowered the exploration of diverse biological questions at the individual cells. Droplet-based single-cell RNA sequencing (scRNA-seq) methods have been particularly widely used due to their high-throughput capabilities and small reaction volumes. While commercial systems have contributed to the widespread adoption of droplet-based scRNA-seq, their relatively high cost limits the ability to profile large numbers of cells and samples. Moreover, as the scale of single-cell sequencing continues to expand, accommodating diverse workflows and cost-effective multi-biospecimen profiling becomes more critical. Herein, we present inDrops-2, an open-source scRNA-seq technology designed to profile live or preserved cells with a sensitivity matching that of state-of-the-art commercial systems but at a 6-fold lower cost. We demonstrate the flexibility of inDrops-2, by implementing two prominent scRNA-seq protocols, based on exponential and linear amplification of barcoded-complementary DNA, and provide useful insights into the advantages and disadvantages inherent to each approach. We applied inDrops-2 to simultaneously profile multiple human lung carcinoma samples that had been subjected to cell preservation, long-term storage and multiplexing to obtain a multiregional cellular profile of the tumor microenvironment. The scalability, sensitivity and cost efficiency make inDrops-2 stand out among other droplet-based scRNA-seq methods, ideal for large-scale studies on rare cell molecular signatures.

Research progress and the prospect of using single-cell sequencing technology to explore the characteristics of the tumor microenvironment

In precision cancer therapy, addressing intra-tumor heterogeneity poses a significant obstacle. Due to the heterogeneity of each cell subtype and between cells within the tumor, the sensitivity and resistance of different patients to targeted drugs, chemotherapy, etc., are inconsistent. Concerning a specific tumor type, many feasible treatments or combinations can be used by specifically targeting the tumor microenvironment. To solve this problem, it is necessary to further study the tumor microenvironment. Single-cell sequencing techniques can dissect distinct tumor cell populations by isolating cells and using statistical computational methods. This technology may assist in the selection of targeted combination therapy, and the obtained cell subset information is crucial for the rational application of targeted therapy. In this review, we summarized the research and application advances of single-cell sequencing technology in the tumor microenvironment, including the most commonly used single-cell genomic and transcriptomic sequencing, and their future development direction was proposed. The application of single-cell sequencing technology has been expanded to include epigenomics, proteomics, metabolomics, and microbiome analysis. The integration of these different omics approaches has significantly advanced the development of single-cell multiomics sequencing technology. This innovative approach holds immense potential for various fields, such as biological research and medical investigations. Finally, we discussed the advantages and disadvantages of using single-cell sequencing to explore the tumor microenvironment.

Challenges and Opportunities in the Clinical Translation of High-Resolution Spatial Transcriptomics

Pathology has always been fueled by technological advances. Histology powered the study of tissue architecture at single-cell resolution and remains a cornerstone of clinical pathology today. In the last decade, next-generation sequencing has become informative for the targeted treatment of many diseases, demonstrating the importance of genome-scale molecular information for personalized medicine. Today, revolutionary developments in spatial transcriptomics technologies digitalize gene expression at subcellular resolution in intact tissue sections, enabling the computational analysis of cell types, cellular phenotypes, and cell-cell communication in routinely collected and archival clinical samples. Here we review how such molecular microscopes work, highlight their potential to identify disease mechanisms and guide personalized therapies, and provide guidance for clinical study design. Finally, we discuss remaining challenges to the swift translation of high-resolution spatial transcriptomics technologies and how integration of multimodal readouts and deep learning approaches is bringing us closer to a holistic understanding of tissue biology and pathology.

Single-Cell Analysis Provides New Insights into the Roles of Tertiary Lymphoid Structures and Immune Cell Infiltration in Kidney Injury and Chronic Kidney Disease

Chronic kidney disease (CKD) is a global health concern with high morbidity and mortality. Acute kidney injury (AKI) is a pivotal risk factor for the progression of CKD, and the rate of AKI-to-CKD progression increases with aging. Intrarenal inflammation is a fundamental mechanism underlying AKI-to-CKD progression. Tertiary lymphoid structures (TLSs), ectopic lymphoid aggregates formed in nonlymphoid organs, develop in aged injured kidneys, but not in young kidneys, with prolonged inflammation and maladaptive repair, which potentially exacerbates AKI-to-CKD progression in aged individuals. Dysregulated immune responses are involved in the pathogenesis of various kidney diseases, such as IgA nephropathy, lupus nephritis, and diabetic kidney diseases, thereby deteriorating kidney function. TLSs also develop in several kidney diseases, including transplanted kidneys and renal cell carcinoma. However, the precise immunologic mechanisms driving AKI-to-CKD progression and development of these kidney diseases remain unclear, which hinders the development of novel therapeutic approaches. This review aims to describe recent findings from single-cell analysis of cellular heterogeneity and complex interactions among immune and renal parenchymal cells, which potentially contribute to the pathogenesis of AKI-to-CKD progression and other kidney diseases, highlighting the mechanisms of formation and pathogenic roles of TLSs in aged injured kidneys.

Quantitative peripheral live single T-cell dynamic polyfunctionality profiling predicts lung cancer checkpoint immunotherapy treatment response and clinical outcomes

Background

Predictive biomarkers for immune checkpoint inhibitors (ICIs), e.g., programmed death ligand-1 (PD-L1) tumor proportional score (TPS), remain limited in clinical applications. Predictive biomarkers that require invasive tumor biopsy procedures are practically challenging especially when longitudinal follow-up is required. Clinical utility of tissue-based PD-L1 TPS also becomes diluted when ICI is combined with chemotherapies. Peripheral single T-cell dynamic polyfunctionality profiling offers the opportunity to reveal rare T-cell subpopulations that are polyfunctional and responsible for the underlying ICI treatment molecular response that bulk biological assays cannot achieve. Here, we evaluated a novel live single-cell functional liquid biopsy cytokine profiling platform, IsoLight, as a potential predictive biomarker to track ICI treatment response and clinical outcomes in non-small cell lung cancer (NSCLC).

Methods

Peripheral blood mononuclear cell samples of 10 healthy donors and 10 NSCLC patients undergoing ICI-based therapies were collected longitudinally pre-/post-ICI treatment after ≥2 cycles under institutional review board (IRB)-approved protocols. Cancer blood samples were collected from unresectable advanced stage (III-IV) NSCLC patients. Clinical course and treatment response and survival outcomes were extracted from electronic health records, with treatment response assessed by treating oncologists based on RECIST. CD4+ and CD8+ T-cells were enriched magnetically and analyzed on the IsoLight platform. Single T-cells were captured in microchambers on IsoCode chips for proteomic immune cytokines profiling. Functional polyfunctionality data from 55,775 single cells were analyzed with IsoSpeak software, 2D- and 3D-t-distributed stochastic neighbor embedding (t-SNE) analysis, kappa coefficient, and Kaplan-Meier survival plots. P values ≤0.05 is considered statistically significant.

Results

Pre-treatment baseline polyfunctionality profiles could not differentiate NSCLC patients from healthy subjects, and could not differentiate ICI responders from non-responders. We found a statistically significant difference between responders and non-responders in CD8+ T-cells' changes in overall polyfunctionality (ΔPolyFx) (P=0.01) and polyfunctional strength index (ΔPSI) (P=0.006) in our dynamic pre-/post-treatment single cell measurements, both performing better than PD-L1 TPS alone (P=0.08). In the 3D-t-SNE analysis, subpopulations of post-treatment CD8+ T-cells in ICI responders displayed distinct immune cytokine profiles from those in pre-treatment cells. CD8+ T-cells ΔPolyFx and ΔPSI scores performed better than PD-L1 TPS in ICI response correlation. Moreover, combined PD-L1 strong TPS and ΔPSI >15 scores strongly correlated with early ICI response with a robust kappa coefficient of 1.0 (P=0.003), which was previously statistically established to indicate a perfect agreement between the prediction and actual response status. Interestingly, high CD4+ T-cells ΔPSI >5 was found to correlate with a strong trend of improved progression-free survival (3.9-fold) (10.8 vs. 2.8 months; P=0.07) and overall survival (3-fold) (34.5 vs. 11.5 months; P=0.09) in ICI-treated patients.

Conclusions

Our study nominates single peripheral T-cell polyfunctionality dynamics analysis to be a promising liquid biopsy platform to determine potential ICI predictive biomarker in NSCLC. It warrants further studies in larger prospective cohorts to validate the clinical utilities and to further optimize cancer immunotherapy.

Color-multiplexed 3D differential phase contrast microscopy with optimal annular illumination

Quantitative phase imaging (QPI) has become a valuable tool in the field of biomedical research due to its ability to quantify refractive index variations of live cells and tissues. For example, three-dimensional differential phase contrast (3D DPC) imaging uses through-focus images captured under different illumination patterns deconvoluted with a computed 3D phase transfer function (PTF) to reconstruct the 3D refractive index. In conventional 3D DPC with semi-circular illumination, partially spatially coherent illumination often diminishes phase contrast, exacerbating inherent noise, and can lead to a large number of zero values in the 3D PTF, resulting in strong low-frequency artifacts and deteriorating imaging resolution. To overcome the above drawbacks, we obtain the conditions for acquiring the optimal 3D PTF based on the analysis of the 3D imaging model and the derivation of the 3D PTF calculation process and propose a 3D DPC microscopy based on optimal annular illumination. The proposed optimal annular illumination pattern minimizes the missing frequency components in the 3D Fourier space, resulting in the best noise-robustness and significantly increased phase contrast. To expedite imaging speed, we utilize a 1/2 annular multiplexed illumination, reducing data acquisition volume by 75%. The 3D refractive index tomography of a simulated 3D phase object, unstained tongue sections, and oral epithelial cells demonstrates that our proposed method achieves the above advantages. In conclusion, we demonstrate a novel 3D DPC microscope that only requires replacing the illumination of a commercial microscope with a programmable LED array. The accurate 3D refractive index tomography and the compactness of the system setup allow the method to play a significant role in the biomedical field.

A novel biomarker of COVI-19: MMP8 emerged by integrated bulk RNAseq and single-cell sequencing

COVID-19 has been emerging as the most influential illness which has caused great costs to the heath of population and social economy. Sivelestat sodium (SS) is indicated as an effective cure for lung dysfunction, a characteristic symptom of COVID-19 infection, but its pharmacological target is still unclear. Therefore, a deep understanding of the pathological progression and molecular alteration is an urgent issue for settling the diagnosis and therapy problems of COVID-19. In this study, the bulk ribonucleic acid sequencing (RNA-seq) data of healthy donors and non-severe and severe COVID-19 patients were collected. Then, target differentially expressed genes (DEGs) were screened through integrating sequencing data and the pharmacological database. Besides, with the help of functional and molecular interaction analyses, the potential effect of target gene alteration on COVID-19 progression was investigated. Single-cell sequencing was performed to evaluate the cell distribution of target genes, and the possible interaction of gene-positive cells with other cells was explored by intercellular ligand-receptor pattern analysis. The results showed that matrix metalloproteinase 8 (MMP8) was upregulated in severe COVID-19 patients, which was also identified as a targeting site to SS. Additionally, MMP8 took a core part in the regulatory interaction network of the screened DEGs in COVID-19 and was dramatically correlated with the inflammatory signaling pathway. The further investigations indicated that MMP8 was mainly expressed in myelocytes with a high degree of heterogeneity. MMP8-positive myelocytes interacted with other cell types through RETN-TLR4 and RETN-CAP1 ligand-receptor patterns. These findings emphasize the important role of MMP8 in COVID-19 progression and provide a potential therapeutic target for COVID-19 patients.

Identification of Macrophage-Related Biomarkers for Abdominal Aortic Aneurysm Through Combined Single-Cell Sequencing and Machine Learning

Purpose

The relationship between macrophages and the progression of abdominal aortic aneurysms (AAA) remains unclear, and effective biomarkers are lacking. In this study, we elucidated the mechanism whereby macrophages promote AAA development and identified associated biomarkers, with the goal of developing new targeted therapies and improving patient outcomes.

Patients and Methods

Differential expression analysis, weighted gene co-expression network analysis, and single-cell analysis were used to identify macrophage-related genes in an AAA dataset. Machine learning algorithms identified THBS1, HCLS1, DMXL2, and ZEB2 as key macrophage-related genes upregulated in AAA; these four hub genes were then used to construct a nomogram as an auxiliary tool for clinical diagnosis. Subsequent downstream single-cell and CellChat analyses were conducted to observe the interactions between macrophages and fibroblasts and analyze potential pathways.

Results

Single-cell validation confirmed enhanced THBS1 expression in macrophages in AAA. CellChat analysis revealed enhanced interactions between macrophages and fibroblasts in AAA through THBS1-CD47 signaling. Finally, an analysis of clinical samples from patients with AAA confirmed the high expression of THBS1 and CD47 in AAA and that THBS1 promotes the progression of AAA through the TNF-NFκB signaling pathway. Our findings reveal the THBS1-CD47 signaling pathway as a critical mechanism in macrophage-driven AAA progression, highlighting THBS1's potential as a therapeutic target.

Conclusion

Our findings highlight THBS1 as a potential driver of macrophage-mediated AAA formation and an important biomarker for AAA diagnosis. The study results would help in improving treatment outcomes in patients with AAA. These findings provide a foundation for the development of diagnostic tools and targeted therapies for AAA, potentially improving early detection and patient outcomes.

Evaluating the Utilities of Foundation Models in Single-cell Data Analysis

Foundation Models (FMs) have made significant strides in both industrial and scientific domains. In this paper, we evaluate the performance of FMs for single-cell sequencing data analysis through comprehensive experiments across eight downstream tasks pertinent to single-cell data. Overall, the top FMs include scGPT, Geneformer, and CellPLM by considering model performances and user accessibility among ten single-cell FMs. However, by comparing these FMs with task-specific methods, we found that single-cell FMs may not consistently excel than task-specific methods in all tasks, which challenges the necessity of developing foundation models for single-cell analysis. In addition, we evaluated the effects of hyper-parameters, initial settings, and stability for training single-cell FMs based on a proposed scEval framework, and provide guidelines for pre-training and fine-tuning, to enhance the performances of single-cell FMs. Our work summarizes the current state of single-cell FMs, points to their constraints and avenues for future development, and offers a freely available evaluation pipeline to benchmark new models and improve method development.

SCInter: A comprehensive single-cell transcriptome integration database for human and mouse

Single-cell RNA sequencing (scRNA-seq), which profiles gene expression at the cellular level, has effectively explored cell heterogeneity and reconstructed developmental trajectories. With the increasing research on diseases and biological processes, scRNA-seq datasets are accumulating rapidly, highlighting the urgent need for collecting and processing these data to support comprehensive and effective annotation and analysis. Here, we have developed a comprehensive Single-Cell transcriptome integration database for human and mouse (SCInter, https://bio.liclab.net/SCInter/index.php), which aims to provide a manually curated database that supports the provision of gene expression profiles across various cell types at the sample level. The current version of SCInter includes 115 integrated datasets and 1016 samples, covering nearly 150 tissues/cell lines. It contains 8016,646 cell markers in 457 identified cell types. SCInter enabled comprehensive analysis of cataloged single-cell data encompassing quality control (QC), clustering, cell markers, multi-method cell type automatic annotation, predicting cell differentiation trajectories and so on. At the same time, SCInter provided a user-friendly interface to query, browse, analyze and visualize each integrated dataset and single cell sample, along with comprehensive QC reports and processing results. It will facilitate the identification of cell type in different cell subpopulations and explore developmental trajectories, enhancing the study of cell heterogeneity in the fields of immunology and oncology.

Omics-Enhanced Nanomedicine for Cancer Therapy

Cancer nanomedicine has emerged as a promising approach to overcome the limitations of conventional cancer therapies, offering enhanced efficacy and safety in cancer management. However, the inherent heterogeneity of tumors presents increasing challenges for the application of cancer nanomedicine in both diagnosis and treatment. This heterogeneity necessitates the integration of advanced and high-throughput analytical techniques to tailor nanomedicine strategies to individual tumor profiles. Omics technologies, encompassing genomics, epigenomics, transcriptomics, proteomics, metabolomics, and more, provide unparalleled insights into the molecular and cellular mechanisms underlying cancer. By dissecting tumor heterogeneity across multiple levels, these technologies offer robust support for the development of personalized and precise cancer nanomedicine strategies. In this review, the principles, techniques, and applications of key omics technologies are summarized. Especially, the synergistic integration of omics and nanomedicine in cancer therapy is explored, focusing on enhanced diagnostic accuracy, optimized therapeutic strategies and the assessment of nanomedicine-mediated biological responses. Moreover, this review addresses current challenges and outlines future directions in the field of omics-enhanced nanomedicine. By offering valuable insights and guidance, this review aims to advance the integration of omics with nanomedicine, ultimately driving improved diagnostic and therapeutic strategies for cancer.

CosGeneGate selects multi-functional and credible biomarkers for single-cell analysis

MOTIVATION

Selecting representative genes or marker genes to distinguish cell types is an important task in single-cell sequencing analysis. Although many methods have been proposed to select marker genes, the genes selected may have redundancy and/or do not show cell-type-specific expression patterns to distinguish cell types.

RESULTS

Here, we present a novel model, named CosGeneGate, to select marker genes for more effective marker selections. CosGeneGate is inspired by combining the advantages of selecting marker genes based on both cell-type classification accuracy and marker gene specific expression patterns. We demonstrate the better performance of the marker genes selected by CosGeneGate for various downstream analyses than the existing methods with both public datasets and newly sequenced datasets. The non-redundant marker genes identified by CosGeneGate for major cell types and tissues in human can be found at the website as follows: https://github.com/VivLon/CosGeneGate/blob/main/marker gene list.xlsx.

Comparative transcriptomic analyses of thymocytes using 10x Genomics and Parse scRNA-seq technologies

BACKGROUND

Single-cell RNA sequencing experiments commonly use 10x Genomics (10x) kits due to their high-throughput capacity and standardized protocols. Recently, Parse Biosciences (Parse) introduced an alternative technology that uses multiple in-situ barcoding rounds within standard 96-well plates. Parse enables the analysis of more cells from multiple samples in a single run without the need for additional reagents or specialized microfluidics equipment. To evaluate the performance of both platforms, we conducted a benchmark study using biological and technical replicates of mouse thymus as a complex immune tissue.

RESULTS

We found that Parse detected nearly twice the number of genes compared to 10x, with each platform detecting a distinct set of genes. The comparison of multiplexed samples generated from 10x and Parse techniques showed 10x data to have lower technical variability and more precise annotation of biological states in the thymus compared to Parse.

CONCLUSION

Our results provide a comprehensive comparison of the suitability of both single-cell platforms for immunological studies.

A Single-Cell Analysis of the NK-Cell Landscape Reveals That Dietary Restriction Boosts NK-Cell Antitumor Immunity via Eomesodermin

Abnormal metabolism in tumor cells represents a potential target for tumor therapy. In this regard, dietary restriction (DR) or its combination with anticancer drugs is of interest as it can impede the growth of tumor cells. In addition to its effects on tumor cells, DR also plays an extrinsic role in restricting tumor growth by regulating immune cells. NK cells are innate immune cells involved in tumor immunosurveillance. However, it remains uncertain whether DR can assist NK cells in controlling tumor growth. In this study, we demonstrate that DR effectively inhibits metastasis of melanoma cells to the lung. Consistent with this, the regression of tumors induced by DR was minimal in mice lacking NK cells. Single-cell RNA sequencing analysis revealed that DR enriched a rejuvenated subset of CD27+CD11b+ NK cells. Mechanistically, DR activated a regulatory network involving the transcription factor Eomesodermin (Eomes), which is essential for NK-cell development. First, DR promoted the expression of Eomes by optimizing mTORC1 signaling. The upregulation of Eomes revived the subset of functional CD27+CD11b+ NK cells by counteracting the expression of T-bet and downstream Zeb2. Moreover, DR enhanced the function and chemotaxis of NK cells by increasing the accessibility of Eomes to chromatin, leading to elevated expression of adhesion molecules and chemokines. Consequently, we conclude that DR therapy enhances tumor immunity through nontumor autonomous mechanisms, including promoting NK-cell tumor immunosurveillance and activation.

Single-cell RNA-seq in diabetic foot ulcer wound healing

Diabetic foot ulcer (DFU) is a chronic and serious complication of diabetes mellitus. It is mainly caused by hyperglycaemia, diabetic peripheral vasculopathy and diabetic peripheral neuropathy. These conditions result in ulceration of foot tissues and chronic wounds. If left untreated, DFU can lead to amputation or even endanger the patient's life. Single-cell RNA sequencing (scRNA-seq) is a technique used to identify and characterise transcriptional subpopulations at the single-cell level. It provides insight into cellular function and the molecular drivers of disease. The objective of this paper is to examine the subpopulations, genes and molecules of cells associated with chronic wounds of diabetic foot by using scRNA-seq. The paper aims to explore the wound-healing mechanism of DFU from three aspects: inflammation, angiogenesis and extracellular matrix remodelling. The goal is to gain a better understanding of the mechanism of DFU wound healing and identify possible DFU therapeutic targets, providing new insights for the application of DFU personalised therapy.

A distinct immune landscape in anti-synthetase syndrome profiled by a single-cell genomic study

Objectives

The objective of this study was to profile the transcriptional profiles of peripheral blood mononuclear cells (PBMCs) and their immune repertoires affected by anti-synthetase syndrome (ASS) at the single-cell level.

Methods

We performed single-cell RNA sequencing (scRNA-seq) analysis of PBMCs and bulk RNA sequencing for patients with ASS (N=3) and patients with anti-melanoma differentiation-associated gene 5-positive dermatomyositis (MDA5+ DM, N=3) along with healthy controls (HCs, N=4). As ASS and MDA5+ DM have similar organ involvements, MDA5+ DM was used as a disease control. The immune repertoire was constructed by reusing the same scRNA-seq datasets. Importantly, flow cytometry was performed to verify the results from the scRNA-seq analysis.

Results

After meticulous annotation of PBMCs, we noticed a significant decrease in the proportion of mucosal-associated invariant T (MAIT) cells in ASS patients compared to HCs, while there was a notable increase in the proportion of proliferative NKT cells. Compared with MDA5+ DM patients, in their PBMCs ASS patients presented substantial enrichment of interferon pathways, which were primarily mediated by IFN-II, and displayed a weak immune response. Furthermore, ASS patients exhibited more pronounced metabolic abnormalities, which may in turn affect oxidative phosphorylation pathways. Monocytes from ASS patients appear to play a crucial role as receptive signaling cells for the TNF pathway. Immunophenotyping analysis of PBMCs from ASS patients revealed an increasing trend for the clone type CQQSYSTPWTF.

Conclusion

Using single-cell genomic datasets of ASS PBMCs, we revealed a distinctive profile in the immune system of individuals with ASS, compared to that with MDA5+ DM or healthy controls.

Towards personalized vaccines

The emergence of vaccinomics and system vaccinology represents a transformative shift in immunization strategies, advocating for personalized vaccines tailored to individual genetic and immunological profiles. Integrating insights from genomics, transcriptomics, proteomics, and immunology, personalized vaccines offer the promise of enhanced efficacy and safety, revolutionizing the field of vaccinology. However, the development of personalized vaccines presents multifaceted challenges, including technical, ethical, economic, and regulatory considerations. Addressing these challenges is essential to ensure equitable access and safety of personalized vaccination strategies. Despite these hurdles, the potential of personalized vaccines to optimize responses and mitigate disease burden underscores the significance of ongoing research and collaboration in advancing precision medicine in immunization.

Loss of AR-regulated AFF3 contributes to prostate cancer progression and reduces ferroptosis sensitivity by downregulating ACSL4 based on single-cell sequencing analysis

Prostate cancer (PCa) is one of the most common cancers affecting the health of men worldwide. Castration-resistant prostate cancer (CRPC), the advanced and refractory phase of prostate cancer, has multiple mechanisms of resistance to androgen deprivation therapy (ADT) such as AR mutations, aberrant androgen synthase, and abnormal expression of AR-related genes. Based on the research of the AR pathway, new drugs for the treatment of CRPC have been developed in clinical practice, such as Abiraterone and enzalutamide. However, many areas in this pathway are still worth exploring. In this study, single-cell sequencing analysis was utilized to scrutinize significant genes in the androgen receptor (AR) pathway related to CRPC. Our analysis of single-cell sequencing combined with bulk-cell sequencing revealed a substantial downregulation of AR-regulated AFF3 in CRPC. Overexpression of AFF3 restricted the proliferation and migration of prostate cancer cells whilst also increasing their sensitivity towards enzalutamide, while knockdown of AFF3 had the opposite effect. To elucidate the mechanism of tumor inhibition by AFF3, we applied GSVA and GSEA to investigate the metabolic pathways related to AFF3 and revealed that AFF3 had an impact on fatty acids metabolism and ferroptosis through the regulation of ACSL4 protein expression. Based on correlation analysis and flow cytometry, we can speculate that AFF3 can impact the sensitivity of the CRPC cell lines to the ferroptosis inducer (RSL3) by regulating ACSL4. Therefore, our findings may provide new insights into the mechanisms of drug resistance in CRPC, and AFF3 may serve as a novel prognostic biomarker in prostate cancer.

Immunological perspectives on atherosclerotic plaque formation and progression

Atherosclerosis serves as the primary catalyst for numerous cardiovascular diseases. Growing evidence suggests that the immune response is involved in every stage of atherosclerotic plaque evolution. Rapid, but not specific, innate immune arms, including neutrophils, monocytes/macrophages, dendritic cells (DCs) and other innate immune cells, as well as pattern-recognition receptors and various inflammatory mediators, contribute to atherogenesis. The specific adaptive immune response, governed by T cells and B cells, antibodies, and immunomodulatory cytokines potently regulates disease activity and progression. In the inflammatory microenvironment, the heterogeneity of leukocyte subpopulations plays a very important regulatory role in plaque evolution. With advances in experimental techniques, the fine mechanisms of immune system involvement in atherosclerotic plaque evolution are becoming known. In this review, we examine the critical immune responses involved in atherosclerotic plaque evolution, in particular, looking at atherosclerosis from the perspective of evolutionary immunobiology. A comprehensive understanding of the interplay between plaque evolution and plaque immunity provides clues for strategically combating atherosclerosis.

Cryo-PRO facilitates whole blood cryopreservation for single-cell RNA sequencing of immune cells from clinical samples

Single-cell RNA sequencing (scRNA-seq) of peripheral blood mononuclear cells (PBMCs) has enhanced our understanding of host immune mechanisms in small cohorts, particularly in diseases with complex and heterogeneous immune responses such as sepsis. However, PBMC isolation from blood requires technical expertise, training, and approximately two hours of onsite processing using Ficoll density gradient separation ('Ficoll') for scRNA-seq compatibility, precluding large-scale sample collection at most clinical sites. To minimize onsite processing, we developed Cryo-PRO (Cryopreservation with PBMC Recovery Offsite), a method of PBMC isolation from cryopreserved whole blood that allows immediate onsite sample cryopreservation and subsequent PBMC isolation in a central laboratory prior to sequencing. We compared scRNA-seq results from samples processed using Cryo-PRO versus standard onsite Ficoll separation in 23 patients with sepsis. Critical scRNA-seq outputs including cell substate fractions and marker genes were similar for each method across multiple cell types and substates, including an important monocyte substate enriched in patients with sepsis (Pearson correlation 0.78, p<0.001; 70% of top marker genes shared). Cryo-PRO reduced onsite sample processing time from >2 hours to <15 minutes and was reproducible across two enrollment sites, thus demonstrating potential for expanding scRNA-seq in multicenter studies of sepsis and other diseases.

B cell tolerance and autoimmunity: Lessons from repertoires

Adaptive immune cell function is regulated by a highly diverse receptor recombined from variable germline-encoded segments that can recognize an almost unlimited array of epitopes. While this diversity enables the recognition of any pathogen, it also poses a risk of self-recognition, leading to autoimmunity. Many layers of regulation are present during both the generation and activation of B cells to prevent this phenomenon, although they are evidently imperfect. In recent years, our ability to analyze immune repertoires at scale has drastically increased, both through advances in sequencing and single-cell analyses. Here, we review the current knowledge on B cell repertoire analyses, focusing on their implication for autoimmunity. These studies demonstrate that a failure of tolerance occurs at multiple independent checkpoints in different autoimmune contexts, particularly during B cell maturation, plasmablast differentiation, and within germinal centers. These failures are marked by distinct repertoire features that may be used to identify disease- or patient-specific therapeutic approaches.

Monocyte Single-Cell Multimodal Profiling in Cardiovascular Disease Risk States

BACKGROUND

Monocytes are a critical innate immune system cell type that serves homeostatic and immunoregulatory functions. They have been identified historically by the cell surface expression of CD14 and CD16. However, recent single-cell studies have revealed that they are much more heterogeneous than previously realized.

METHODS

We utilized cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) and single-cell RNA sequencing to describe the comprehensive transcriptional and phenotypic landscape of 437 126 monocytes.

RESULTS

This high-dimensional multimodal approach identified vast phenotypic diversity and functionally distinct subsets, including IFN-responsive, MHCIIhi (major histocompatibility complex class II), monocyte-platelet aggregates, as well as nonclassical, and several subpopulations of classical monocytes. Using flow cytometry, we validated the existence of MHCII+CD275+ MHCIIhi, CD42b+ monocyte-platelet aggregates, CD16+CD99- nonclassical monocytes, and CD99+ classical monocytes. Each subpopulation exhibited unique characteristics, developmental trajectories, transcriptional regulation, and tissue distribution. In addition, alterations associated with cardiovascular disease risk factors, including race, smoking, and hyperlipidemia were identified. Moreover, the effect of hyperlipidemia was recapitulated in mouse models of elevated cholesterol.

CONCLUSIONS

This integrative and cross-species comparative analysis provides a new perspective on the comparison of alterations in monocytes in pathological conditions and offers insights into monocyte-driven mechanisms in cardiovascular disease and the potential for monocyte subpopulation targeted therapies.

Advances in single-cell transcriptomics in animal research

Understanding biological mechanisms is fundamental for improving animal production and health to meet the growing demand for high-quality protein. As an emerging biotechnology, single-cell transcriptomics has been gradually applied in diverse aspects of animal research, offering an effective method to study the gene expression of high-throughput single cells of different tissues/organs in animals. In an unprecedented manner, researchers have identified cell types/subtypes and their marker genes, inferred cellular fate trajectories, and revealed cell‒cell interactions in animals using single-cell transcriptomics. In this paper, we introduce the development of single-cell technology and review the processes, advancements, and applications of single-cell transcriptomics in animal research. We summarize recent efforts using single-cell transcriptomics to obtain a more profound understanding of animal nutrition and health, reproductive performance, genetics, and disease models in different livestock species. Moreover, the practical experience accumulated based on a large number of cases is highlighted to provide a reference for determining key factors (e.g., sample size, cell clustering, and cell type annotation) in single-cell transcriptomics analysis. We also discuss the limitations and outlook of single-cell transcriptomics in the current stage. This paper describes the comprehensive progress of single-cell transcriptomics in animal research, offering novel insights and sustainable advancements in agricultural productivity and animal health.

Interactions between immune cell types facilitate the evolution of immune traits

An essential prerequisite for evolution by natural selection is variation among individuals in traits that affect fitness1. The ability of a system to produce selectable variation, known as evolvability2, thus markedly affects the rate of evolution. Although the immune system is among the fastest-evolving components in mammals3, the sources of variation in immune traits remain largely unknown4,5. Here we show that an important determinant of the immune system's evolvability is its organization into interacting modules represented by different immune cell types. By profiling immune cell variation in bone marrow of 54 genetically diverse mouse strains from the Collaborative Cross6, we found that variation in immune cell frequencies is polygenic and that many associated genes are involved in homeostatic balance through cell-intrinsic functions of proliferation, migration and cell death. However, we also found genes associated with the frequency of a particular cell type that are expressed in a different cell type, exerting their effect in what we term cyto-trans. The vertebrate evolutionary record shows that genes associated in cyto-trans have faced weaker negative selection, thus increasing the robustness and hence evolvability2,7,8 of the immune system. This phenomenon is similarly observable in human blood. Our findings suggest that interactions between different components of the immune system provide a phenotypic space in which mutations can produce variation with little detriment, underscoring the role of modularity in the evolution of complex systems9.

Direct observation correlates NFκB cRel in B cells with activating and terminating their proliferative program

Antibody responses require the proliferative expansion of B cells controlled by affinity-dependent signals. Yet, proliferative bursts are heterogeneous, varying between 0 and 8 divisions in response to the same stimulus. NFκB cRel is activated in response to immune stimulation in B cells and is genetically required for proliferation. Here, we asked whether proliferative heterogeneity is controlled by natural variations in cRel abundance. We developed a fluorescent reporter mTFP1-cRel for the direct observation of cRel in live proliferating B cells. We found that cRel is heterogeneously distributed among naïve B cells, which are enriched for high expressors in a heavy-tailed distribution. We found that high cRel expressors show faster activation of the proliferative program, but do not sustain it well, with population expansion decaying earlier. With a mathematical model of the molecular network, we showed that cRel heterogeneity arises from balancing positive feedback by autoregulation and negative feedback by its inhibitor IκBε, confirmed by mouse knockouts. Using live-cell fluorescence microscopy, we showed that increased cRel primes B cells for early proliferation via higher basal expression of the cell cycle driver cMyc. However, peak cMyc induction amplitude is constrained by incoherent feedforward regulation, decoding the fold change of cRel activity to terminate the proliferative burst. This results in a complex nonlinear, nonmonotonic relationship between cRel expression and the extent of proliferation. These findings emphasize the importance of direct observational studies to complement gene knockout results and to learn about quantitative relationships between biological processes and their key regulators in the context of natural variations.

An atlas of transcribed enhancers across helper T cell diversity for decoding human diseases

Transcribed enhancer maps can reveal nuclear interactions underpinning each cell type and connect specific cell types to diseases. Using a 5' single-cell RNA sequencing approach, we defined transcription start sites of enhancer RNAs and other classes of coding and noncoding RNAs in human CD4+ T cells, revealing cellular heterogeneity and differentiation trajectories. Integration of these datasets with single-cell chromatin profiles showed that active enhancers with bidirectional RNA transcription are highly cell type-specific and that disease heritability is strongly enriched in these enhancers. The resulting cell type-resolved multimodal atlas of bidirectionally transcribed enhancers, which we linked with promoters using fine-scale chromatin contact maps, enabled us to systematically interpret genetic variants associated with a range of immune-mediated diseases.

Picoliter Single-Cell Reactor for Proteome Profiling by In Situ Cell Lysis, Protein Immobilization, Digestion, and Droplet Transfer

Global profiling of single-cell proteomes can reveal cellular heterogeneity, thus benefiting precision medicine. However, current mass spectrometry (MS)-based single-cell proteomic sample processing still faces technical challenges associated with processing efficiency and protein recovery. Herein, we present an innovative sample processing platform based on a picoliter single-cell reactor (picoSCR) for single-cell proteome profiling, which involves in situ protein immobilization and sample transfer. PicoSCR helped minimize surface adsorptive losses by downscaling the processing volume to 400 pL with a contact area of less than 0.4 mm2. Besides, picoSCR reached highly efficient cell lysis and digestion within 30 min, benefiting from optimal reagent and high reactant concentrations. Using the picoSCR-nanoLC-MS system, over 1400 proteins were identified from an individual HeLa cell using data-dependent acquisition mode. Proteins with copy number below 1000 were identified, demonstrating this system with a detection limit of 1.7 zmol. Furthermore, we profiled the proteome of circulating tumor cells (CTCs). Data are available via ProteomeXchange with the identifier PXD051468. Proteins associated with epithelial-mesenchymal transition and neutrophil extracellular traps formation (which are both related to tumor metastasis) were observed in all CTCs. The cellular heterogeneity was revealed by differences in signaling pathways within individual cells. These results highlighted the potential of the picoSCR platform to help discover new biomarkers and explore differences in biological processes between cells.

[Applications of high performance liquid chromatography-mass spectrometry in proteomics]

Proteomics profiling plays an important role in biomedical studies. Proteomics studies are much more complicated than genome research, mainly because of the complexity and diversity of proteomic samples. High performance liquid chromatography-mass spectrometry (HPLC-MS) is a fundamental tool in proteomics research owing to its high speed, resolution, and sensitivity. Proteomics research targets from the peptides and individual proteins to larger protein complexes, the molecular weight of which gradually increases, leading to sustained increases in structural and compositional complexity and alterations in molecular properties. Therefore, the selection of various separation strategies and stationary-phase parameters is crucial when dealing with the different targets in proteomics research for in-depth proteomics analysis. This article provides an overview of commonly used chromatographic-separation strategies in the laboratory, including reversed-phase liquid chromatography (RPLC), hydrophilic interaction liquid chromatography (HILIC), hydrophobic interaction chromatography (HIC), ion-exchange chromatography (IEC), and size-exclusion chromatography (SEC), as well as their applications and selectivity in the context of various biomacromolecules. At present, no single chromatographic or electrophoretic technology features the peak capacity required to resolve such complex mixtures into individual components. Multidimensional liquid chromatography (MDLC), which combines different orthogonal separation modes with MS, plays an important role in proteomics research. In the MDLC strategy, IEC, together with RPLC, remains the most widely used separation mode in proteomics analysis; other chromatographic methods are also frequently used for peptide/protein fractionation. MDLC technologies and their applications in a variety of proteomics analyses have undergone great development. Two strategies in MDLC separation systems are mainly used in proteomics profiling: the "bottom-up" approach and the "top-down" approach. The "shotgun" method is a typical "bottom-up" strategy that is based on the RPLC or MDLC separation of whole-protein-sample digests coupled with MS; it is an excellent technique for identifying a large number of proteins. "Top-down" analysis is based on the separation of intact proteins and provides their detailed molecular information; thus, this technique may be advantageous for analyzing the post-translational modifications (PTMs) of proteins. In this paper, the "bottom-up" "top-down" and protein-protein interaction (PPI) analyses of proteome samples are briefly reviewed. The diverse combinations of different chromatographic modes used to set up MDLC systems are described, and compatibility issues between mobile phases and analytes, between mobile phases and MS, and between mobile phases in different separation modes in multidimensional chromatography are analyzed. Novel developments in MDLC techniques, such as high-abundance protein depletion and chromatography arrays, are further discussed. In this review, the solutions proposed by researchers when encountering compatibility issues are emphasized. Moreover, the applications of HPLC-MS combined with various sample pretreatment methods in the study of exosomal and single-cell proteomics are examined. During exosome isolation, the combined use of ultracentrifugation and SEC can yield exosomes of higher purity. The use of SEC with ultra-large-pore-size packing materials (200 nm) enables the isolation of exosomal subgroups, and proteomics studies have revealed significant differences in protein composition and function between these subgroups. In the field of single-cell proteomics, researchers have addressed challenges related to reducing sample processing volumes, preventing sample loss, and avoiding contamination during sample preparation. Innovative methods and improvements, such as the utilization of capillaries for sample processing and microchips as platforms to minimize the contact area of the droplets, have been proposed. The integration of these techniques with HPLC-MS shows some progress. In summary, this article focuses on the recent advances in HPLC-MS technology for proteomics analysis and provides a comprehensive reference for future research in the field of proteomics.

GENIX enables comparative network analysis of single-cell RNA sequencing to reveal signatures of therapeutic interventions

Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of cellular responses to perturbations such as therapeutic interventions and vaccines. Gene relevance to such perturbations is often assessed through differential expression analysis (DEA), which offers a one-dimensional view of the transcriptomic landscape. This method potentially overlooks genes with modest expression changes but profound downstream effects and is susceptible to false positives. We present GENIX (gene expression network importance examination), a computational framework that transcends DEA by constructing gene association networks and employing a network-based comparative model to identify topological signature genes. We benchmark GENIX using both synthetic and experimental datasets, including analysis of influenza vaccine-induced immune responses in peripheral blood mononuclear cells (PBMCs) from recovered COVID-19 patients. GENIX successfully emulates key characteristics of biological networks and reveals signature genes that are missed by classical DEA, thereby broadening the scope of target gene discovery in precision medicine.

scEM: A New Ensemble Framework for Predicting Cell Type Composition Based on scRNA-Seq Data

With the advent of single-cell RNA sequencing (scRNA-seq) technology, many scRNA-seq data have become available, providing an unprecedented opportunity to explore cellular composition and heterogeneity. Recently, many computational algorithms for predicting cell type composition have been developed, and these methods are typically evaluated on different datasets and performance metrics using diverse techniques. Consequently, the lack of comprehensive and standardized comparative analysis makes it difficult to gain a clear understanding of the strengths and weaknesses of these methods. To address this gap, we reviewed 20 cutting-edge unsupervised cell type identification methods and evaluated these methods comprehensively using 24 real scRNA-seq datasets of varying scales. In addition, we proposed a new ensemble cell-type identification method, named scEM, which learns the consensus similarity matrix by applying the entropy weight method to the four representative methods are selected. The Louvain algorithm is adopted to obtain the final classification of individual cells based on the consensus matrix. Extensive evaluation and comparison with 11 other similarity-based methods under real scRNA-seq datasets demonstrate that the newly developed ensemble algorithm scEM is effective in predicting cellular type composition.

Uncover spatially informed variations for single-cell spatial transcriptomics with STew

Motivation

The recent spatial transcriptomics (ST) technologies have enabled characterization of gene expression patterns and spatial information, advancing our understanding of cell lineages within diseased tissues. Several analytical approaches have been proposed for ST data, but effectively utilizing spatial information to unveil the shared variation with gene expression remains a challenge.

Results

We introduce STew, a Spatial Transcriptomic multi-viEW representation learning method, to jointly analyze spatial information and gene expression in a scalable manner, followed by a data-driven statistical framework to measure the goodness of model fit. Through benchmarking using human dorsolateral prefrontal cortex and mouse main olfactory bulb data with true manual annotations, STew achieved superior performance in both clustering accuracy and continuity of identified spatial domains compared with other methods. STew is also robust to generate consistent results insensitive to model parameters, including sparsity constraints. We next applied STew to various ST data acquired from 10× Visium, Slide-seqV2, and 10× Xenium, encompassing single-cell and multi-cellular resolution ST technologies, which revealed spatially informed cell type clusters and biologically meaningful axes. In particular, we identified a proinflammatory fibroblast spatial niche using ST data from psoriatic skins. Moreover, STew scales almost linearly with the number of spatial locations, guaranteeing its applicability to datasets with thousands of spatial locations to capture disease-relevant niches in complex tissues.

Availability and implementation

Source code and the R software tool STew are available from github.com/fanzhanglab/STew.

Integrative analysis of COL6A3 in lupus nephritis: insights from single-cell transcriptomics and proteomics

Introduction

Lupus nephritis (LN), a severe complication of systemic lupus erythematosus (SLE), presents significant challenges in patient management and treatment outcomes. The identification of novel LN-related biomarkers and therapeutic targets is critical to enhancing treatment outcomes and prognosis for patients.

Methods

In this study, we analyzed single-cell expression data from LN (n=21) and healthy controls (n=3). A total of 143 differentially expressed genes were identified between the LN and control groups. Then, proteomics analysis of LN patients (n=9) and control (SLE patients without LN, n=11) revealed 55 differentially expressed genes among patients with LN and control group. We further utilizes protein-protein interaction network and functional enrichment analyses to elucidate the pivotal role of COL6A3 in key signaling pathways. Its diagnostic value is evaluate through its correlation with disease progression and renal function metrics, as well as Receiver Operating Characteristic Curve (ROC) analysis. Additionally, immunohistochemistry and qPCR experiments were performed to validate the expression of COL6A3 in LN.

Results

By comparison of single-cell and proteomics data, we discovered that COL6A3 is significantly upregulated, highlighting it as a critical biomarker of LN. Our findings emphasize the substantial involvement of COL6A3 in the pathogenesis of LN, particularly noting its expression in mesangial cells. Through comprehensive protein-protein interaction network and functional enrichment analyses, we uncovered the pivotal role of COL6A3 in key signaling pathways including integrin-mediated signaling pathways, collagen-activated signaling pathways, and ECM-receptor interaction, suggesting potential therapeutic targets. The diagnostic utility is confirmed by its correlation with disease progression and renal function metrics of the glomerular filtration rate. ROC analysis further validates the diagnostic value of COL6A3, with the area under the ROC values of 0.879 in the in-house cohort, and 0.802 and 0.915 in tubular and glomerular external cohort samples, respectively. Furthermore, immunohistochemistry and qPCR experiments were consistent with those obtained from the single-cell RNA sequencing and proteomics studies.

Discussion

These results proved that COL6A3 is a promising biomarker and therapeutic target, advancing personalized medicine strategies for LN.

Advanced sequencing-based high-throughput and long-read single-cell transcriptome analysis

Cells are the fundamental building blocks of living systems, exhibiting significant heterogeneity. The transcriptome connects the cellular genotype and phenotype, and profiling single-cell transcriptomes is critical for uncovering distinct cell types, states, and the interplay between cells in development, health, and disease. Nevertheless, single-cell transcriptome analysis faces daunting challenges due to the low abundance and diverse nature of RNAs in individual cells, as well as their heterogeneous expression. The advent and continuous advancements of next-generation sequencing (NGS) and third-generation sequencing (TGS) technologies have solved these problems and facilitated the high-throughput, sensitive, full-length, and rapid profiling of single-cell RNAs. In this review, we provide a broad introduction to current methodologies for single-cell transcriptome sequencing. First, state-of-the-art advancements in high-throughput and full-length single-cell RNA sequencing (scRNA-seq) platforms using NGS are reviewed. Next, TGS-based long-read scRNA-seq methods are summarized. Finally, a brief conclusion and perspectives for comprehensive single-cell transcriptome analysis are discussed.

Exploring the role of gut microbes in spondyloarthritis: Implications for pathogenesis and therapeutic strategies

The gut microbiota plays a pivotal role in regulating host immunity, and dysregulation of this interaction is implicated in autoimmune and inflammatory diseases, including spondyloarthritis (SpA). This review explores microbial dysbiosis and altered metabolic function observed in various forms of SpA, such as ankylosing spondylitis (AS), psoriatic arthritis (PsA), acute anterior uveitis (AAU), and SpA-associated gut inflammation. Studies on animal models and clinical samples highlight the association between gut microbial dysbiosis, metabolic perturbations and immune dysregulation in SpA pathogenesis. These studies have received impetus through next-generation sequencing methods, which have enabled the characterization of gut microbial composition and function, and host gene expression. Microbial/metabolomic studies have revealed potential biomarkers and therapeutic targets, such as short-chain fatty acids, and tryptophan metabolites, offering insights into disease mechanisms and treatment approaches. Further studies on microbial function and its modulation of the immune response have uncovered molecular mechanisms underlying various SpA. Understanding the complex interplay between microbial community structure and function holds promise for improved diagnosis and management of SpA and other autoimmune disorders.

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.