Intratumoral T-cell composition predicts epcoritamab-based treatment efficacy in B-cell non-Hodgkin lymphomas.

Leveraging endogenous tumor-resident T-cells for immunotherapy using bispecific antibodies (BsAb) targeting CD20 and CD3 has emerged as a promising therapeutic strategy for patients with B-cell non-Hodgkin lymphomas. However, features associated with treatment response or resistance are unknown. To this end, we analyzed data from patients treated with epcoritamab-containing regimens in the EPCORE NHL-2 trial (NCT04663347). We observed downregulation of CD20 expression on B-cells following treatment initiation both in progressing patients and in patients achieving durable complete responses (CR), suggesting that CD20 downregulation does not universally predict resistance to BsAb-based therapy. Single-cell immune profiling of tumor biopsies obtained following one cycle of therapy revealed substantial clonal expansion of cytotoxic CD4+ and CD8+ T-cells in patients achieving CR, and an expansion of follicular helper and regulatory CD4+ T-cells in patients whose disease progressed. These results identify distinct tumor-resident T-cell profiles associated with response or resistance to BsAb therapy.

SingleCellGGM enables gene expression program identification from single-cell transcriptomes and facilitates universal cell label transfer.

Gene co-expression analysis of single-cell transcriptomes, aiming to define functional relationships between genes, is challenging due to excessive dropout values. Here, we developed a single-cell graphical Gaussian model (SingleCellGGM) algorithm to conduct single-cell gene co-expression network analysis. When applied to mouse single-cell datasets, SingleCellGGM constructed networks from which gene co-expression modules with highly significant functional enrichment were identified. We considered the modules as gene expression programs (GEPs). These GEPs enable direct cell-type annotation of individual cells without cell clustering, and they are enriched with genes required for the functions of the corresponding cells, sometimes at levels greater than 10-fold. The GEPs are conserved across datasets and enable universal cell-type label transfer across different studies. We also proposed a dimension-reduction method through averaging by GEPs for single-cell analysis, enhancing the interpretability of results. Thus, SingleCellGGM offers a unique GEP-based perspective to analyze single-cell transcriptomes and reveals biological insights shared by different single-cell datasets.

Adhesive interactions within microbial consortia can be differentiated at the single-cell level through expansion microscopy.

Investigating microbe-microbe interactions at the single-cell level is critical to unraveling the ecology and dynamics of microbial communities. In many situations, microbes assemble themselves into densely packed multi-species biofilms. The density and complexity pose acute difficulties for visualizing individual cells and analyzing their interactions. Here, we address this problem through an unconventional application of expansion microscopy, which allows for the 'decrowding' of individual bacterial cells within a multispecies community. Expansion microscopy generally has been carried out under isotropic expansion conditions and used as a resolution-enhancing method. In our variation of expansion microscopy, we carry out expansion under heterotropic conditions; that is, we expand the space between bacterial cells but not the space within individual cells. The separation of individual bacterial cells from each other reflects the competition between the expansion force pulling them apart and the adhesion force holding them together. We employed heterotropic expansion microscopy to study the relative strength of adhesion in model biofilm communities. These included mono and dual-species Streptococcus biofilms, and a three-species synthetic community (Fusobacterium nucleatum, Streptococcus mutans, and Streptococcus sanguinis) under conditions that facilitated interspecies coaggregation. Using adhesion mutants, we investigated the interplay between F. nucleatum outer membrane protein RadD and different Streptococcus species. We also examined the Schaalia-TM7 epibiont association. Quantitative proximity analysis was used to evaluate the separation of individual microbial members. Our study demonstrates that heterotropic expansion microscopy can 'decrowd' dense biofilm communities, improve visualization of individual bacterial members, and enable analysis of microbe-microbe adhesive interactions at the single-cell level.

HAO2 protects from proximal tubular cells injured in rats with chronic kidney disease by promoting fatty acid metabolic processes.

The complex pathogenesis of kidney disease is closely related to the diversity of kidney intrinsic cells. In this study, single-cell transcriptome sequencing technology was used to sequence and analyze blood and kidney tissue cells in normal control rats and rats with chronic kidney disease (CKD), focusing on key cell populations and functional enrichment to explore the pathogenesis of CKD. Oil red O staining and enzyme-linked immunosorbent assay (ELISA) were used to detect lipid droplets and free fatty acid (FFA). Quantitative real-time polymerase chain reaction (RT-PCR), western blot (WB) were used to verify the differential gene hydroxyacid oxidase 2 (HAO2) and fatty acid metabolic process in tissue to ensure the reliability of single-cell sequencing results. We successfully established a single-cell transcriptome atlas of blood and kidney tissue in rats with CKD, which were annotated into 14 cell subsets (MPCs, PT, Tc, DCT, B-IC, A-IC, CNT, ALOH, BC, Neu, Endo, Pla, NKT, Baso) according to marker gene, and the integrated single-cell atlas of rats showed a significant increase and decrease of MPCs and PTs in the CKD group, respectively. Functional analysis found extensive enrichment of metabolic-related pathways in PT cells, includes fatty acid metabolic process, cellular amino acid metabolic process and generation of precursor metabolites and energy. Immunohistochemical experiments determined that the differential gene HAO2 was localized in the renal tubules, and its expression was significantly reduced in CKD group compared with control, and oil red O staining showed that lipid droplets increased in the CKD group, after overexpression of HAO2 the lipid droplets was inhibited. ELISA assay showed that ATP content decreased in the CKD group and FFA increased in the CKD group. Moreover, the mitochondrial membrane potential of the cells in the OE-HAO2 group was significantly increased compared with OE-NC. The acyl-CoA oxidase 1(ACOX1), peroxisome proliferator-activated receptor alpha (PPARα), peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC1α) were decreased in the CKD group, while genes and proteins were increased after overexpression of HAO2, and the AMP-activated protein kinase (AMPK) phosphorylated proteins were increased, the acetyl-CoA carboxylase (ACC) phosphorylated proteins were decreased, reversely. Therefore, HAO2 may be an important regulator of fatty acid metabolic processes in CKD, and overexpression of HAO2 can enhance fatty acid metabolism by promoting fatty acid oxidation (FAO) pathway.

Macrophage iron dyshomeostasis promotes aging-related renal fibrosis.

Renal aging, marked by the accumulation of senescent cells and chronic low-grade inflammation, leads to renal interstitial fibrosis and impaired function. In this study, we investigate the role of macrophages, a key regulator of inflammation, in renal aging by analyzing kidney single-cell RNA sequencing data of C57BL/6J mice from 8 weeks to 24 months. Our findings elucidate the dynamic changes in the proportion of kidney cell types during renal aging and reveal that increased macrophage infiltration contributes to chronic low-grade inflammation, with these macrophages exhibiting senescence and activation of ferroptosis signaling. CellChat analysis indicates enhanced communications between macrophages and tubular cells during aging. Suppressing ferroptosis alleviates macrophage-mediated tubular partial epithelial-mesenchymal transition in vitro, thereby mitigating the expression of fibrosis-related genes. Using SCENIC analysis, we infer Stat1 as a key age-related transcription factor promoting iron dyshomeostasis and ferroptosis in macrophages by regulating the expression of Pcbp1, an iron chaperone protein that inhibits ferroptosis. Furthermore, through virtual screening and molecular docking from a library of anti-aging compounds, we construct a docking model targeting Pcbp1, which indicates that the natural small molecule compound Rutin can suppress macrophage senescence and ferroptosis by preserving Pcbp1. In summary, our study underscores the crucial role of macrophage iron dyshomeostasis and ferroptosis in renal aging. Our results also suggest Pcbp1 as an intervention target in aging-related renal fibrosis and highlight Rutin as a potential therapeutic agent in mitigating age-related renal chronic low-grade inflammation and fibrosis.

Intracellular Flow Cytometry ("Phosphoflow") to Assess Signal Transduction in Rare Populations Such As Memory B Cell Subsets and Plasma Cells.

Intracellular flow cytometry is a powerful technique that can be used to interrogate signalling in rare cellular populations. The strengths of the technique are that massively parallel readouts can be gained from thousands of single cells simultaneously, and the assay is fast and relatively straightforward. This plate-based protocol enables different doses and different timepoints of stimulation to be assessed and has been optimized for rare B cell populations. Combining this technique with high-dimensional flow cytometry enables multiple signalling proteins to be measured with high confidence.

Single-cell RNA-seq reveals the transcriptional program underlying tumor progression and metastasis in neuroblastoma.

Neuroblastoma (NB) is one of the most common childhood malignancies. Sixty percent of patients present with widely disseminated clinical signs at diagnosis and exhibit poor outcomes. However, the molecular mechanisms triggering NB metastasis remain largely uncharacterized. In this study, we generated a transcriptomic atlas of 15 447 NB cells from eight NB samples, including paired samples of primary tumors and bone marrow metastases. We used time-resolved analysis to chart the evolutionary trajectory of NB cells from the primary tumor to the metastases in the same patient and identified a common 'starter' subpopulation that initiates tumor development and metastasis. The 'starter' population exhibited high expression levels of multiple cell cycle-related genes, indicating the important role of cell cycle upregulation in NB tumor progression. In addition, our evolutionary trajectory analysis demonstrated the involvement of partial epithelial-to-mesenchymal transition (p-EMT) along the metastatic route from the primary site to the bone marrow. Our study provides insights into the program driving NB metastasis and presents a signature of metastasis-initiating cells as an independent prognostic indicator and potential therapeutic target to inhibit the initiation of NB metastasis.

Investigating the characteristics of mild intervertebral disc degeneration at various age stages using single-cell genomics.

Introduction: Intervertebral disc degeneration often occurs in the elderly population, but in recent years, there has been an increasing incidence of disc degeneration in younger individuals, primarily with mild degeneration. Methods: In order to explore the underlying mechanisms of disc degeneration in both young and aging individuals, we collected four types of nucleus pulposus (NP) single-cell sequencing samples for analysis based on Pfirrmann grading: normal-young (NY) (Grade I), normal-old (NO) (Grade I), mild degenerative-young (MY) (Grade II-III), and mild degenerative-old (MO) (Grade II-III). Results: We found that most NP cells in NO and MY samples exhibited oxidative stress, which may be important pathogenic factors in NO and MY groups. On the other hand, NP cells in MO group exhibited endoplasmic reticulum stress. In terms of inflammation, myeloid cells were mainly present in the degenerative group, with the MY group showing a stronger immune response compared to the MO group. Interestingly, dendritic cells in the myeloid lineage played a critical role in the process of mild degeneration. Discussion: Our study investigated the molecular mechanisms of intervertebral disc degeneration from an age perspective, providing insights for improving treatment strategies for patients with disc degeneration at different age groups.

Protocol for simultaneous isolation of high-quality and high-quantity cardiomyocytes and non-myocyte cells from adult rat hearts.

Isolating high-quality different cell types is a powerful approach for understanding cellular compositions and features in the heart, but it is challenging. The available protocols typically focus on isolating one or two cell types. Here, we present a protocol to simultaneously isolate high-quality and high-quantity cardiomyocytes and non-myocyte cells, including immune cells, from adult rat hearts. We describe steps for purifying cells using bovine serum albumin. We also detail procedures for viability analysis and cell type identification using fluorescence-activated cell sorting. For complete details on the use and execution of this protocol, please refer to Zhang et al.,1 Valkov et al.,2 Vang et al.,3 and Li et al.4.

A 3D micro-printed single cell micro-niche with asymmetric niche signals - An in vitro model for asymmetric cell division study.

Intricate microenvironment signals orchestrate to affect cell behavior and fate during tissue morphogenesis. However, the underlying mechanisms on how specific local niche signals influence cell behavior and fate are not fully understood, owing to the lack of in vitro platform able to precisely, quantitatively, spatially, and independently manipulate individual niche signals. Here, microarrays of protein-based 3D single cell micro-niche (3D-SCµN), with precisely engineered biophysical and biochemical niche signals, are micro-printed by a multiphoton microfabrication and micropatterning technology. Mouse embryonic stem cell (mESC) is used as the model cell to study how local niche signals affect stem cell behavior and fate. By precisely engineering the internal microstructures of the 3D SCµNs, we demonstrate that the cell division direction can be controlled by the biophysical niche signals, in a cell shape-independent manner. After confining the cell division direction to a dominating axis, single mESCs are exposed to asymmetric biochemical niche signals, specifically, cell-cell adhesion molecule on one side and extracellular matrix on the other side. We demonstrate that, symmetry-breaking (asymmetric) niche signals successfully trigger cell polarity formation and bias the orientation of asymmetric cell division, the mitosis process resulting in two daughter cells with differential fates, in mESCs.

Single-Cell Synchro-Subtractive-Additive Nanoscale Surgery with Femtosecond Lasers.

Herein, an in situ "synchro-subtractive-additive" technique of femtosecond laser single-cell surgery (FLSS) is presented to address the inadequacies of existing surgical methods for single-cell manipulation. This process is enabled by synchronized nanoscale three-dimensional (3D) subtractive and additive manufacturing with ultrahigh precision on various parts of the cells, in that the precise removal and modification of a single-cell structure are realized by nonthermal ablation, with synchronously ultrafast solidification of the specially designed hydrogel by two photopolymerizations. FLSS is a minimally invasive technique with a post-operative survival rate of 70% and stable proliferation. It opens avenues for bottom-up synthetic biology, offering new methods for artificially synthesizing organelle-like 3D structures and modifying the physiological activities of cells.

Single Cell RNAseq Analysis of Cytokine-Treated Human Islets: Association of Cellular Stress with Impaired Cytokine Responsiveness.

Pancreatic ß-cells are essential for survival, being the only cell type capable of insulin secretion. While they are believed to be vulnerable to damage by inflammatory cytokines such as interleukin-1 beta (IL-1ß) and interferon-gamma, we have recently identified physiological roles for cytokine signaling in rodent ß-cells that include the stimulation of antiviral and antimicrobial gene expression and the inhibition of viral replication. In this study, we examine cytokine-stimulated changes in gene expression in human islets using single-cell RNA sequencing. Surprisingly, the global responses of human islets to cytokine exposure were remarkably blunted compared to our previous observations in the mouse. The small population of human islet cells that were cytokine responsive exhibited increased expression of IL-1ß-stimulated antiviral guanylate-binding proteins, just like in the mouse. Most human islet cells were not responsive to cytokines, and this lack of responsiveness was associated with high expression of genes encoding ribosomal proteins. We further correlated the expression levels of RPL5 with stress response genes, and when expressed at high levels, RPL5 is predictive of failure to respond to cytokines in all endocrine cells. We postulate that donor causes of death and isolation methodologies may contribute to stress of the islet preparation. Our findings indicate that activation of stress responses in human islets limits cytokine-stimulated gene expression, and we urge caution in the evaluation of studies that have examined cytokine-stimulated gene expression in human islets without evaluation of stress-related gene expression.

DeepGRNCS: deep learning-based framework for jointly inferring gene regulatory networks across cell subpopulations.

Inferring gene regulatory networks (GRNs) allows us to obtain a deeper understanding of cellular function and disease pathogenesis. Recent advances in single-cell RNA sequencing (scRNA-seq) technology have improved the accuracy of GRN inference. However, many methods for inferring individual GRNs from scRNA-seq data are limited because they overlook intercellular heterogeneity and similarities between different cell subpopulations, which are often present in the data. Here, we propose a deep learning-based framework, DeepGRNCS, for jointly inferring GRNs across cell subpopulations. We follow the commonly accepted hypothesis that the expression of a target gene can be predicted based on the expression of transcription factors (TFs) due to underlying regulatory relationships. We initially processed scRNA-seq data by discretizing data scattering using the equal-width method. Then, we trained deep learning models to predict target gene expression from TFs. By individually removing each TF from the expression matrix, we used pre-trained deep model predictions to infer regulatory relationships between TFs and genes, thereby constructing the GRN. Our method outperforms existing GRN inference methods for various simulated and real scRNA-seq datasets. Finally, we applied DeepGRNCS to non-small cell lung cancer scRNA-seq data to identify key genes in each cell subpopulation and analyzed their biological relevance. In conclusion, DeepGRNCS effectively predicts cell subpopulation-specific GRNs. The source code is available at https://github.com/Nastume777/DeepGRNCS.

Characterizing efficient feature selection for single-cell expression analysis.

Unsupervised feature selection is a critical step for efficient and accurate analysis of single-cell RNA-seq data. Previous benchmarks used two different criteria to compare feature selection methods: (i) proportion of ground-truth marker genes included in the selected features and (ii) accuracy of cell clustering using ground-truth cell types. Here, we systematically compare the performance of 11 feature selection methods for both criteria. We first demonstrate the discordance between these criteria and suggest using the latter. We then compare the distribution of selected genes in their means between feature selection methods. We show that lowly expressed genes exhibit seriously high coefficients of variation and are mostly excluded by high-performance methods. In particular, high-deviation- and high-expression-based methods outperform the widely used in Seurat package in clustering cells and data visualization. We further show they also enable a clear separation of the same cell type from different tissues as well as accurate estimation of cell trajectories.

Integrated analysis of single-cell sequencing and machine learning identifies a signature based on monocyte/macrophage hub genes to analyze the intracranial aneurysm associated immune microenvironment.

Monocytes are pivotal immune cells in eliciting specific immune responses and can exert a significant impact on the progression, prognosis, and immunotherapy of intracranial aneurysms (IAs). The objective of this study was to identify monocyte/macrophage (Mo/MΦ)-associated gene signatures to elucidate their correlation with the pathogenesis and immune microenvironment of IAs, thereby offering potential avenues for targeted therapy against IAs. Single-cell RNA-sequencing (scRNA-seq) data of IAs were acquired from the Gene Expression Synthesis (GEO) database. The significant infiltration of monocyte subsets in the parietal tissue of IAs was identified using single-cell RNA sequencing and high-dimensional weighted gene co-expression network analysis (hdWGCNA). The integration of six machine learning algorithms identified four crucial genes linked to these Mo/MΦ. Subsequently, we developed a multilayer perceptron (MLP) neural model for the diagnosis of IAs (independent external test AUC=1.0, sensitivity =100%, specificity =100%). Furthermore, we employed the CIBERSORT method and MCP counter to establish the correlation between monocyte characteristics and immune cell infiltration as well as patient heterogeneity. Our findings offer valuable insights into the molecular characterization of monocyte infiltration in IAs, which plays a pivotal role in shaping the immune microenvironment of IAs. Recognizing this characterization is crucial for comprehending the limitations associated with targeted therapies for IAs. Ultimately, the results were verified by real-time fluorescence quantitative PCR and Immunohistochemistry.

Spatiotemporal single-cell analysis decodes cellular dynamics underlying different responses to immunotherapy in colorectal cancer.

Expanding the efficacy of immune checkpoint blockade (ICB) in colorectal cancer (CRC) presses for a comprehensive understanding of treatment responsiveness. Here, we analyze multiple sequential single-cell samples from 22 patients undergoing PD-1 blockade to map the evolution of local and systemic immunity of CRC patients. In tumors, we identify coordinated cellular programs exhibiting distinct response associations. Specifically, exhausted T (Tex) or tumor-reactive-like CD8+ T (Ttr-like) cells are closely related to treatment efficacy, and Tex cells show correlated proportion changes with multiple other tumor-enriched cell types following PD-1 blockade. In addition, we reveal the less-exhausted phenotype of blood-associated Ttr-like cells in tumors and find that their higher abundance suggests better treatment outcomes. Finally, a higher major histocompatibility complex (MHC) II-related signature in circulating CD8+ T cells at baseline is linked to superior responses. Our study provides insights into the spatiotemporal cellular dynamics following neoadjuvant PD-1 blockade in CRC.

Are We There Yet? Assessing the Readiness of Single-Cell Proteomics to Answer Biological Hypotheses.

Single-cell analysis is an active area of research in many fields of biology. Measurements at single-cell resolution allow researchers to study diverse populations without losing biologically meaningful information to sample averages. Many technologies have been used to study single cells, including mass spectrometry-based single-cell proteomics (SCP). SCP has seen a lot of growth over the past couple of years through improvements in data acquisition and analysis, leading to greater proteomic depth. Because method development has been the main focus in SCP, biological applications have been sprinkled in only as proof-of-concept. However, SCP methods now provide significant coverage of the proteome and have been implemented in many laboratories. Thus, a primary question to address in our community is whether the current state of technology is ready for widespread adoption for biological inquiry. In this Perspective, we examine the potential for SCP in three thematic areas of biological investigation: cell annotation, developmental trajectories, and spatial mapping. We identify that the primary limitation of SCP is sample throughput. As proteome depth has been the primary target for method development to date, we advocate for a change in focus to facilitate measuring tens of thousands of single-cell proteomes to enable biological applications beyond proof-of-concept.

Genomic, transcriptomic and epigenomic analysis towards the understanding of porcine semen quality traits. Past, current and future trends.

The importance of boar reproductive traits, including semen quality, in the sustainability of pig production system is increasingly being acknowledged by academic and industrial sectors. Research is needed to understand the biology and genetic components underlying these traits so that they can be incorporated into selection schemes and managerial decisions. This article reviews our current understanding of genome biology and technologies for genome, transcriptome and epigenome analysis which now facilitate the identification of causal variants affecting phenotypes more than ever before. Genetic and transcriptomic analysis of candidate genes, Genome-Wide Association Studies, expression microarrays, RNA-Seq of coding and noncoding genes and epigenomic evaluations have been conducted to profile the molecular makeups of pig sperm. These studies have provided insightful information for a several semen-related parameters. Nonetheless, this research is still incipient. The spermatozoon harbors a reduced transcriptome and highly modified epigenome, and it is assumed to be transcriptionally silent for nuclear gene expression. For this reason, the extent to which the sperm's RNA and epigenome recapitulate sperm biology and function is unclear. Hence, we anticipate that single-cell level analyses of the testicle and other male reproductive organs, which can reveal active transcription and epigenomic profiles in cells influencing sperm quality, will gain popularity and markedly advance our understanding of sperm-related traits. Future research will delve deeper into sperm fertility, boar resilience to environmental changes or harsh conditions, especially in the context of global warming, and also in transgenerational inheritance and how the environment influences the sperm transcriptome and epigenome.