Construction of a human cell landscape at single-cell level

Exploring the protective mechanisms of syringaresinol against myocardial infarction by experimental validation and network pharmacology

Myocardial Infarction (MI) is a leading cause of mortality worldwide. Currently, effective treatments are still lacking. Increasing evidence supports the benefits of Syringaresinol (SYR) for the treatment of cardiovascular disease is accumulating. Nevertheless, whether SYR can alleviate MI is unknown. The study aims to investigate the protective effect of SYR against MI and elucidate its potential molecular mechanism. We found that SYR ameliorate MI-induced cardiac dysfunction, reduce infarct size, and alleviate myocardial hypertrophy, fibrosis, inflammation, as well as apoptosis. In addition, we collected targets related to SYR and MI through multiple databases, and obtained 281 potential therapeutic targets after intersection. GO and KEGG enrichment analysis found that these therapeutic targets were concentrated on inflammation, fibrosis, and apoptosis pathways. Based on the PPI network and combined with Centiscape2.2 and cytoHubba analysis, we obtained 10 hub proteins. The molecular docking results showed that SYR has strong bindings with 10 hub proteins. snRNA-seq data showed that CASP3 and NFKB1 were expressed in all cell types. In addition, the therapeutic targets of SYR are also mainly distributed in all cell types. Finally, we found that SYR could alleviate MI by partially reversing the expression of AKT1, EGFR, CASP3, SRC, NFKB1, HSP90AA1, HIF1A, MMP9 and ESR1 both in vivo and in vitro. Our findings suggested that SYR may protect against MI by reducing inflammatory, fibrotic and apoptotic effects via multiple targets and pathways, which provides a new theoretical foundation for the clinical therapy of MI.

A 3D-printed microdevice encapsulates vascularized islets composed of iPSC-derived β-like cells and microvascular fragments for type 1 diabetes treatment

Transplantation of insulin-secreting cells provides a promising method for re-establishing the autonomous blood glucose control ability of type 1 diabetes (T1D) patients, but the low survival of the transplanted cells hinder the therapeutic efficacy. In this study, we 3D-printed an encapsulation system containing β-like cells and microvascular fragments (MVF), to create a retrivable microdevice with vascularized islets in vivo for T1D therapy. The functional β-like cells were differentiated from the urine epithelial cell-derived induced pluripotent stem cells (UiPSCs). Single-cell RNA sequencing provided an integrative study and macroscopic developmental analyses of the entire process of differentiation, which revealed the developmental trajectory of differentiation in vitro follows the developmental pattern of embryonic pancreas in vivo. The MVF were isolated from the epididymal fat pad. The microdevice with a groove structure were rapidly fabricated by the digital light processing (DLP)-3D printing technology. The β-like cells and MVF were uniformly distributed in the device. After subcutaneous transplantation into C57BL/6 mice, the microdevice have less collagen accumulation and low immune cell infiltration. Moreover, the microdevice encapsulated vascularized islets reduced hyperglycemia in 33 % of the treated mice for up to 100 days without immunosuppressants, and the humanized C-peptide was also detected in the serum of the mice. In summary, we described the microdevice-protected vascularized islets for long-term treatment of T1D, with high safety and potential clinical transformative value, and may therefore provide a translatable solution to advance the research progress of β cell replacement therapy for T1D.

Down-regulation of ATP8B2 in Foam Cells Inhibits Autophagic Flux and ox-LDL Degradation in Atherosclerosis

Our study aims to screen and explore the potential molecular mechanisms of atherosclerosis using a comprehensive research approach combining bioinformatics analysis and molecular biology experiments. Bioinformatics analyses were conducted to screen for key genes with significantly differential expression in atherosclerosis. Subsequently, macrophages and foam cells induced from THP-1 cells were utilized to validate the function of these key genes through siRNA knockdown. Molecular biology experiments encompassed reverse transcription polymerase chain reaction (RT-PCR), Western Blotting, immunofluorescence staining, and JC-1 probe detection of mitochondrial membrane potential. ATP8B2, encoding a P4-ATPase, was significantly downregulated in both plaque tissues and circulating macrophages of atherosclerosis patients. This enzyme influences membrane fusion and other dynamic processes by affecting the asymmetric distribution of phospholipids within the bilayer. Knockdown of ATP8B2 expression significantly inhibited autophagic flux in macrophages, manifested by abnormal accumulation of LC3-II and p62 protein levels. Furthermore, downregulation of ATP8B2 expression significantly inhibited the degradation of oxidized low-density lipoprotein (ox-LDL) by macrophages. Simultaneously, reduced ATP8B2 expression led to decreased mitochondrial membrane potential and mitochondrial dysfunction. Our study unveils for the first time the crucial role of ATP8B2 in atherosclerosis, particularly in maintaining autophagic flux, promoting ox-LDL degradation, and sustaining mitochondrial homeostasis.

Single-cell transcriptomic analysis reveals dynamic changes in the liver microenvironment during colorectal cancer metastatic progression

BACKGROUND

Metastasis is a leading cause of cancer-related deaths, with the liver being the most frequent site of metastasis in colorectal cancer. Previous studies have predominantly focused on the influence of the primary tumor itself on metastasis, with relatively limited research examining the changes within target organs.

METHODS

Using an orthotopic mouse model of colorectal cancer, single-cell sequencing was employed to profile the transcriptomic landscape of pre-metastatic and metastatic livers. The analysis focused on identifying cellular and molecular changes within the hepatic microenvironment, with particular emphasis on inflammatory pathways and immune cell populations.

RESULTS

A neutrophil subpopulation with high Prok2 expression was identified, showing elevated levels in the pre-metastatic and metastatic liver. Increased infiltration of Prok2⁺ neutrophils correlated with poor prognosis in liver metastatic colorectal cancer patients. In the liver metastatic niche (MN), these neutrophils showed high App and Cd274 (PD-L1) expression, suppressing macrophage phagocytosis and promoting T-cell exhaustion.

CONCLUSION

A Prok2⁺ neutrophil subpopulation infiltrated both pre-metastatic and macro-metastatic liver environments, potentially driving immunosuppression through macrophage inhibition and T-cell exhaustion. Targeting Prok2⁺ neutrophils could represent a novel therapeutic strategy for preventing liver metastasis in colorectal cancer patients.

Endothelial transcriptomic, epigenomic and proteomic data challenge the proposed role for TSAd in vascular permeability

The vascular endothelial growth factor VEGF drives excessive vascular permeability to cause tissue-damaging oedema in neovascular and inflammatory diseases across multiple organs. Several molecular pathways have been implicated in VEGF-induced hyperpermeability, including binding of the VEGF-activated tyrosine kinase receptor VEGFR2 by the T-cell specific adaptor (TSAd) to recruit a SRC family kinase to induce junction opening between vascular endothelial cells (ECs). Inconsistent with a universal role for TSAd in permeability signalling, immunostaining approaches previously reported TSAd only in dermal and kidney vasculature. To address this discrepancy, we have mined publicly available omics data for expression of TSAd and other permeability-relevant signal transducers in multiple organs affected by VEGF-induced vascular permeability. Unexpectedly, TSAd transcripts were largely absent from EC single cell RNAseq data, whereas transcripts for other permeability-relevant signal transducers were detected readily. TSAd transcripts were also lacking from half of the EC bulk RNAseq datasets examined, and in the remaining datasets appeared at low levels concordant with models of leaky transcription. Epigenomic EC data located the TSAd promoter to closed chromatin in ECs, and mass spectrometry-derived EC proteomes typically lacked TSAd. By suggesting that TSAd is not actively expressed in ECs, our findings imply that TSAd is likely not critical for linking VEGFR2 to downstream signal transducers for EC junction opening.

The Landscape of Malignant Transition: Unraveling Cancer Cell-of-Origin and Heterogeneous Tissue Microenvironment

Understanding disease progression and sophisticated tumor ecosystems is imperative for investigating tumorigenesis mechanisms and developing novel prevention strategies. Here, we dissected heterogeneous microenvironments during malignant transitions by leveraging data from 1396 samples spanning 13 major tissues. Within transitional stem-like subpopulations highly enriched in precancers and cancers, we identified 30 recurring cellular states strongly linked to malignancy, including hypoxia and epithelial senescence, revealing a high degree of plasticity in epithelial stem cells. By characterizing dynamics in stem-cell crosstalk with the microenvironment along the pseudotime axis, we found differential roles of ANXA1 at different stages of tumor development. In precancerous stages, reduced ANXA1 levels promoted monocyte differentiation toward M1 macrophages and inflammatory responses, whereas during malignant progression, upregulated ANXA1 fostered M2 macrophage polarization and cancer-associated fibroblast transformation by increasing TGF-β production. Our spatiotemporal analysis further provided insights into mechanisms responsible for immunosuppression and a potential target to control evolution of precancer and mitigate the risk for cancer development.

Valproic acid-induced teratogenicity is driven by senescence and prevented by Rapamycin in human spinal cord and animal models

Valproic acid (VPA) is an effective and widely used anti-seizure medication but is teratogenic when used during pregnancy, affecting brain and spinal cord development for reasons that remain largely unclear. Here we designed a genetic recombinase-based SOX10 reporter system in human pluripotent stem cells that enables tracking and lineage tracing of Neural Crest cells (NCCs) in a human organoid model of the developing neural tube. We found that VPA induces extensive cellular senescence and promotes mesenchymal differentiation of human NCCs. We next show that the clinically approved drug Rapamycin inhibits senescence and restores aberrant NCC differentiation trajectory after VPA exposure in human organoids and in developing zebrafish, highlighting the therapeutic promise of this approach. Finally, we identify the pioneer factor AP1 as a key element of this process. Collectively our data reveal cellular senescence as a central driver of VPA-associated neurodevelopmental teratogenicity and identifies a new pharmacological strategy for prevention. These results exemplify the power of genetically modified human stem cell-derived organoid models for drug discovery.

Identification and validation of a novel signature based on immune‑related genes from epithelial cells to predict prognosis and treatment response in patients with lung squamous cell cancer by integrated analysis of single‑cell and bulk RNA sequencing

Epithelial cells are associated with tumor immunity through interstitial transformation, yet the role of epithelial immune-related genes (EIGs) in this process remains unclear. Comprehending the mechanisms behind EIGs within lung squamous cell carcinoma (LUSC) may offer an explanation to these issues. The present study aimed to explore the biological role of EIGs in patients with LUSC. Based on data from the Gene Expression Omnibus and The Cancer Genome Atlas databases, a survival model and nomogram was established. This model and nomogram were used to study the mechanism of EIGs in LUSC and its medical significance by enrichment analysis, tumor microenvironment, immune cell infiltration and immune function correlation analysis. Finally, reverse transcription-quantitative PCR (RT-qPCR) and external dataset were used to assess the expression of the EIGs. The survival model was used to develop 4 EIGs as predictors for patient outcomes. Survival curves revealed that higher risk patients had more negative outcomes. This model and the nomogram developed based entirely on this model had an accurate prognosis predictive LUSC. The enrichment analysis indicated that pathways related to antigen processing and presentation, as well as Epstein-Barr virus infection, were prevalent in the high-risk populations. The research on immune infiltration demonstrated a notable rise in activated dendritic cells and neutrophils in the high-risk group. Furthermore, the results revealed that the high-risk populations are particularly susceptible to the effects of afureserpine, gefitinib and savolitinib. Finally, the outcomes of RT-qPCR were consistent with those of the bioinformatics analysis. In conclusion, the risk evaluation model and nomogram are effective in forecasting the prognosis and guiding drug selection for patients with LUSC. A worse prognosis in patients with high risk may be associated with certain viral infections and antigen processing and presentation.

Integrative single-cell and spatial transcriptome analysis reveals heterogeneity of human liver progenitor cells

BACKGROUND

Liver progenitor cells (LPCs) with bipotential differentiation capacities are essential for restoring liver homeostasis and hepatocyte population after damage. However, the low proportion and shared markers with epithelial cells make studying LPC heterogeneity difficult, especially in humans. To address this gap, we explored over 259,400 human liver single cells across 4 conditions (fetal, healthy, cirrhotic, and HCC-affected livers).

METHODS

Human liver tissue samples were analyzed using spatial transcriptomics sequencing technologies to describe the heterogeneity of LPCs. Liver tissue was characterized by LPC heterogeneity at single-cell resolution by employing cellular modules, differentiation trajectories, and gene co-expression patterns.

RESULTS

We annotated and identified 1 LPC cluster, 3 LPC subpopulations, and 4 distinct cellular modules, indicating the heterogeneity within LPC and the diversity between LPCs and epithelial cells. LPCs showed spatial colocalization with cholangiocytes and comprised a small proportion (2.95±1.91%) within the merged epithelial cells and LPC populations, exhibiting marked differences in marker expression patterns compared to those in mice. LPCs exhibited distinct cellular states in functional restoration, activation, proliferation, and cell transition. Additionally, the gene co-expression network of LPCs exhibited 3 unique modules, reflecting the distinct connectivity of genes encoding apolipoproteins and heat shock proteins in the gene co-expression network modules.

CONCLUSIONS

Our study provides valuable insights into the multifaceted heterogeneity of human LPCs. Future studies focusing on spatial gene expression dynamics will contribute to our understanding of the spatial arrangement of liver regeneration.

Single-Cell RNA Sequencing Reveals the Tumor Heterogeneity and Immunosuppressive Microenvironment in Urothelial Carcinoma

Urothelial carcinoma (UC) can arise from either the lower urinary tract or the upper tract; they represent different disease entities and require different clinical treatment strategies. A full understanding of the cellular characteristics in UC may guide the development of novel therapies. Here, we performed single-cell transcriptome analysis from four patients with UC of the bladder (UCB), five patients with UC of the ureter (UCU), and four patients with UC of the renal pelvis (UCRP) to develop a comprehensive cell atlas of UC. We found the rare epithelial cell subtype EP9 with epithelial-to-mesenchymal transition (EMT) and cancer stem cell (CSC) features, and specifically expressed SOX6, which was associated with poor prognosis. We also found that ACKR1+ endothelial cells and inflammatory cancer-associated fibroblasts (iCAFs) were more enriched in UCU, which may promote pathogenesis. While ESM1+ endothelial cells may more actively participate in UCB and UCRP tumorigenesis by promoting angiogenesis. Additionally, CD8 + effector T cells were more enriched in UCU and UCRP patients, while Tregs were mainly enriched in UCB tumors. C1QC+ macrophages and LAMP3+ dendritic cells were more enriched in UCB, which is closely related to the formation of the heterogeneous immunosuppressive microenvironment. Furthermore, we found strong interactions between iCAFs, EP9, and Endo_ESM1, and different degrees of activation of the FGF-FGFR3 axis and immune checkpoint pathway were observed in different UC subtypes. Our study elucidated the cellular heterogeneity and the components of the microenvironment in UC arising from the upper and lower urinary tracts and provided novel therapeutic targets.

Unraveling aging from transcriptomics

Research into aging constitutes a pivotal endeavor aimed at elucidating the underlying biological mechanisms governing aging and age-associated diseases, as well as promoting healthy longevity. Recent advances in transcriptomic technologies, such as bulk RNA sequencing (RNA-seq), single-cell transcriptomics, and spatial transcriptomics, have revolutionized our ability to study aging at unprecedented resolution and scale. These technologies present novel opportunities for the discovery of biomarkers, elucidation of molecular pathways, and development of targeted therapeutic strategies for age-related disorders. This review surveys recent breakthroughs in different types of transcripts on aging, such as mRNA, long noncoding (lnc)RNA, tRNA, and miRNA, highlighting key findings and discussing their potential implications for future studies in this field.

The landscape of cell lineage tracing

Cell fate changes play a crucial role in the processes of natural development, disease progression, and the efficacy of therapeutic interventions. The definition of the various types of cell fate changes, including cell expansion, differentiation, transdifferentiation, dedifferentiation, reprogramming, and state transitions, represents a complex and evolving field of research known as cell lineage tracing. This review will systematically introduce the research history and progress in this field, which can be broadly divided into two parts: prospective tracing and retrospective tracing. The initial section encompasses an array of methodologies pertaining to isotope labeling, transient fluorescent tracers, non-fluorescent transient tracers, non-fluorescent genetic markers, fluorescent protein, genetic marker delivery, genetic recombination, exogenous DNA barcodes, CRISPR-Cas9 mediated DNA barcodes, and base editor-mediated DNA barcodes. The second part of the review covers genetic mosaicism, genomic DNA alteration, TCR/BCR, DNA methylation, and mitochondrial DNA mutation. In the final section, we will address the principal challenges and prospective avenues of enquiry in the field of cell lineage tracing, with a particular focus on the sequencing techniques and mathematical models pertinent to single-cell genetic lineage tracing, and the value of pursuing a more comprehensive investigation at both the spatial and temporal levels in the study of cell lineage tracing.

Heterogeneity of the tumor immune cell microenvironment revealed by single-cell sequencing in head and neck cancer

Head and neck cancer (HNC) is the sixth most common disease in the world. The recurrence rate of patients is relatively high, and the heterogeneity of tumor immune microenvironment (TIME) cells may be an important reason for this. Single-cell sequencing (SCS) is currently the most promising and mature application in cancer research. It can identify unique genes expressed in cells and study tumor heterogeneity. According to current research, the heterogeneity of immune cells has become an important factor affecting the occurrence and development of HNC. SCSs can provide effective therapeutic targets and prognostic factors for HNC patients through analyses of gene expression levels and cell heterogeneity. Therefore, this study analyzes the basic theory of HNC and the development of SCS technology, elaborating on the application of SCS technology in HNC and its potential value in identifying HNC therapeutic targets and biomarkers.

Cross-species imputation and comparison of single-cell transcriptomic profiles

Cross-species comparison and prediction of gene expression profiles are important to understand regulatory changes during evolution and to transfer knowledge learned from model organisms to humans. Single-cell RNA-seq (scRNA-seq) profiles enable us to capture gene expression profiles with respect to variations among individual cells; however, cross-species comparison of scRNA-seq profiles is challenging because of data sparsity, batch effects, and the lack of one-to-one cell matching across species. Moreover, single-cell profiles are challenging to obtain in certain biological contexts, limiting the scope of hypothesis generation. Here we developed Icebear, a neural network framework that decomposes single-cell measurements into factors representing cell identity, species, and batch factors. Icebear enables accurate prediction of single-cell gene expression profiles across species, thereby providing high-resolution cell type and disease profiles in under-characterized contexts. Icebear also facilitates direct cross-species comparison of single-cell expression profiles for conserved genes that are located on the X chromosome in eutherian mammals but on autosomes in chicken. This comparison, for the first time, revealed evolutionary and diverse adaptations of X-chromosome upregulation in mammals.

Hamid SS. Multiomics in silico analysis identifies TM4SF4 as a cell surface target in hepatocellular carcinoma

The clinical application of cellular immunotherapy in hepatocellular carcinoma (HCC) is impeded by the lack of a cell surface target frequently expressed in HCC cells and with minimal presence in normal tissues to reduce on-target, off-tumor toxicity. To address this, an in silico multomics analysis was conducted to identify an optimal therapeutic target in HCC. A longlist of genes (n = 12,948) expressed in HCCs according to The Human Protein Atlas database were examined. Eight genes were shortlisted to identify one with the highest expression in HCCs, without being shed into circulation, and with restrictive expression profile in other normal human tissues. A total of eight genes were shortlisted and subsequently ranked according to the combination of their transcript and protein expression levels in HCC cases (n = 791) derived from four independent datasets. TM4SF4 was the top-ranked target with the highest expression in HCCs. TM4SF4 showed more favorable expression profile with significantly lower expression in normal human tissues but more highly expressed in HCC compared with seven other common HCC therapeutic targets. Furthermore, scRNA-seq and immunohistochemistry datasets showed that TM4SF4 was absent in immune cell populations but highly expressed in the bile duct canaliculi of hepatocytes, regions inaccessible to immune cells. In scRNA-seq dataset of HCCs, TM4SF4 expression was positively associated with mitochondrial components and oxidative phosphorylation Gene Ontologies in HCC cells (n = 15,787 cells), suggesting its potential roles in mitochondrial-mediated oncogenic effects in HCC. Taken together, TM4SF4 is proposed as a promising cell surface target in HCC due to its high expression in HCC cells with restricted expression profile in non-cancerous tissues, and association with HCC oncogenic pathways.

Unveiling the cell-type-specific landscape of cellular senescence through single-cell transcriptomics using SenePy

Senescent cells accumulate in most tissues with organismal aging, exposure to stressors, or disease progression. It is challenging to identify senescent cells because cellular senescence signatures and phenotypes vary widely across distinct cell types and tissues. Here we developed an analytical algorithm that defines cell-type-specific and universal signatures of cellular senescence across a wide range of cell types and tissues. We utilize 72 mouse and 64 human weighted single-cell transcriptomic signatures of cellular senescence to create the SenePy scoring platform. SenePy signatures better recapitulate in vivo cellular senescence than signatures derived from in vitro senescence studies. We use SenePy to map the kinetics of senescent cell accumulation in healthy aging as well as multiple disease contexts, including tumorigenesis, inflammation, and myocardial infarction. SenePy characterizes cell-type-specific in vivo cellular senescence and could lead to the identification of genes that serve as mediators of cellular senescence and disease progression.

scCobra allows contrastive cell embedding learning with domain adaptation for single cell data integration and harmonization

The rapid advancement of single-cell technologies has created an urgent need for effective methods to integrate and harmonize single-cell data. Technical and biological variations across studies complicate data integration, while conventional tools often struggle with reliance on gene expression distribution assumptions and over-correction. Here, we present scCobra, a deep generative neural network designed to overcome these challenges through contrastive learning with domain adaptation. scCobra effectively mitigates batch effects, minimizes over-correction, and ensures biologically meaningful data integration without assuming specific gene expression distributions. It enables online label transfer across datasets with batch effects, allowing continuous integration of new data without retraining. Additionally, scCobra supports batch effect simulation, advanced multi-omic integration, and scalable processing of large datasets. By integrating and harmonizing datasets from similar studies, scCobra expands the available data for investigating specific biological problems, improving cross-study comparability, and revealing insights that may be obscured in isolated datasets.

A deep learning framework for in silico screening of anticancer drugs at the single-cell level

Tumor heterogeneity plays a pivotal role in tumor progression and resistance to clinical treatment. Single-cell RNA sequencing (scRNA-seq) enables us to explore heterogeneity within a cell population and identify rare cell types, thereby improving our design of targeted therapeutic strategies. Here, we use a pan-cancer and pan-tissue single-cell transcriptional landscape to reveal heterogeneous expression patterns within malignant cells, precancerous cells, as well as cancer-associated stromal and endothelial cells. We introduce a deep learning framework named Shennong for in silico screening of anticancer drugs for targeting each of the landscape cell clusters. Utilizing Shennong, we could predict individual cell responses to pharmacologic compounds, evaluate drug candidates' tissue damaging effects, and investigate their corresponding action mechanisms. Prioritized compounds in Shennong's prediction results include FDA-approved drugs currently undergoing clinical trials for new indications, as well as drug candidates reporting anti-tumor activity. Furthermore, the tissue damaging effect prediction aligns with documented injuries and terminated discovery events. This robust and explainable framework has the potential to accelerate the drug discovery process and enhance the accuracy and efficiency of drug screening.

Peroxisome proliferator-activated receptor delta and liver diseases

Peroxisome proliferator-activated receptors (PPARs) are nuclear receptors involved in transcriptional regulation and play an important role in many physiological and metabolic processes. Unlike PPAR-alpha and PPAR-gamma, PPAR-delta is ubiquitously expressed, and its activity is key to maintaining proper metabolic homeostasis within the liver. PPAR-delta not only regulates physiologic processes of lipid, glucose, and bile acid metabolism but also attenuates pathologic responses to alcohol metabolism, inflammation, fibrosis, and carcinogenesis, and is considered an important therapeutic target in liver diseases. Promising results have been reported in clinical trials for PPAR-delta agonists in liver disease, and the selective agonist seladelpar was recently conditionally approved in the United States as a new treatment option for primary biliary cholangitis. This review provides an overview of PPAR-delta's function and biology in the liver, examines its kinetics and therapeutic potential across different liver diseases, and discusses the current status of clinical trials involving its agonists.

Targeting secretory autophagy in solid cancers: mechanisms, immune regulation and clinical insights

Secretory autophagy is a classical form of unconventional secretion that integrates autophagy with the secretory process, relying on highly conserved autophagy-related molecules and playing a critical role in tumor progression and treatment resistance. Traditional autophagy is responsible for degrading intracellular substances by fusing autophagosomes with lysosomes. However, secretory autophagy uses autophagy signaling to mediate the secretion of specific substances and regulate the tumor microenvironment (TME). Cytoplasmic substances are preferentially secreted rather than directed toward lysosomal degradation, involving various selective mechanisms. Moreover, substances released by secretory autophagy convey biological signals to the TME, inducing immune dysregulation and contributing to drug resistance. Therefore, elucidating the mechanisms underlying secretory autophagy is essential for improving clinical treatments. This review systematically summarizes current knowledge of secretory autophagy, from initiation to secretion, considering inter-tumor heterogeneity, explores its role across different tumor types. Furthermore, it proposes future research directions and highlights unresolved clinical challenges.

The dynamics of hematopoiesis over the human lifespan

Over a lifetime, hematopoietic stem cells (HSCs) adjust their lineage output to support age-aligned physiology. In model organisms, stereotypic waves of hematopoiesis have been observed corresponding to defined age-biased HSC hallmarks. However, how the properties of hematopoietic stem and progenitor cells change over the human lifespan remains unclear. To address this gap, we profiled individual transcriptome states of human hematopoietic stem and progenitor cells spanning gestation, maturation and aging. Here we define the gene expression networks dictating age-specific differentiation of HSCs and the dynamics of fate decisions and lineage priming throughout life. We additionally identifiy and functionally validate a fetal-specific HSC state with robust engraftment and multilineage capacity. Furthermore, we observe that classification of acute myeloid leukemia against defined transcriptional age states demonstrates that utilization of early life transcriptional programs associates with poor prognosis. Overall, we provide a disease-relevant framework for heterochronic orientation of stem cell ontogeny along the real time axis of the human lifespan.

Mucosal-Associated Invariant T (MAIT) Cell-Mediated Immune Mechanisms of Peritoneal Dialysis-Induced Peritoneal Fibrosis and Therapeutic Targeting

Key Points

Background

Peritoneal fibrosis is a serious complication of long-term peritoneal dialysis (PD) and abdominal surgeries, yet effective treatments remain elusive. Given the known roles of mucosal-associated invariant T (MAIT) cells in immune responses and fibrotic diseases, we investigated their involvement in PD-induced peritoneal fibrosis to identify potential therapeutic targets.

Methods

We used single-cell RNA sequencing and flow cytometry to characterize the activation and function of peritoneal MAIT cells in patients undergoing long-term PD. Our investigation focused on the molecular pathways activated by these cells, particularly the MHC class 1–related protein 1 (MR1)-mediated interaction with mesothelial cells and subsequent activation of the mTOR complex 1 signaling pathway. We further assessed the effect of inhibiting MAIT cells on fibrogenesis using both in vitro models and Mr1 knockout mice.

Results

Our study revealed that long-term PD significantly enhanced the activation of MAIT cells, particularly the proinflammatory MAIT17 subtype. These activated cells contributed to peritoneal fibrogenesis by binding to the MR1 receptor on mesothelial cells, which triggered hyperglycolysis through the mTOR complex 1 pathway, ultimately leading to fibrogenesis. Notably, we demonstrated that blocking the MR1–MAIT interaction, either through genetic knockout or pharmacological inhibition with acetyl-6-formylpterin, effectively mitigated fibrosis.

Conclusions

This study identified MAIT cells as crucial drivers of PD-induced peritoneal fibrosis.

Recent advances in pancreatic α-cell transdifferentiation for diabetes therapy

As the global prevalence of diabetes mellitus rises, traditional treatments like insulin therapy and oral hypoglycemic agents often fail to achieve optimal glycemic control, leading to severe complications. Recent research has focused on replenishing pancreatic β-cells through the transdifferentiation of α-cells, offering a promising therapeutic avenue. This review explores the molecular mechanisms underlying α-cell to β-cell transdifferentiation, emphasizing key transcription factors such as Dnmt1, Arx, Pdx1, MafA, and Nkx6.1. The potential clinical applications, especially in type 1 and type 2 diabetes characterized by significant β-cell dysfunction, are addressed. Challenges, including low transdifferentiation efficiency, cell stability, and safety concerns, are also included. Future research directions include optimizing molecular pathways, enhancing transdifferentiation efficiency, and ensuring the long-term stability of β-cell identity. Overall, the ability to convert α-cells into β-cells represents a transformative strategy for diabetes treatment, offering hope for more effective and sustainable therapies for patients with severe β-cell loss.

Single-cell transcriptome atlas of lamprey exploring Natterin- induced white adipose tissue browning

Lampreys are early jawless vertebrates that are the key to understanding the evolution of vertebrates. However, the lack of cytomic studies on multiple lamprey organs has hindered progress in this field. Therefore, the present study constructed a comprehensive cell atlas comprising 604,460 cells/nuclei and 70 cell types from 14 lamprey tissue samples. Comparison of cellular evolution across species revealed that most lamprey cell types are homologous to those in jawed vertebrates. We discovered acinar- and islet-like cell populations despite the lack of parenchymal organs in lampreys, providing evidence of pancreatic function in vertebrates. Furthermore, we investigated the heterogeneity of lamprey immune cell populations. Natterin was highly expressed in granulocytes, and NATTERIN was localized to the lipid droplets. Moreover, we developed a transgenic mouse model expressing Natterin to elucidate the role of NATTERIN in lipid metabolism, whereas the browning of white adipose tissue was induced. These findings elucidate vertebrate cellular evolution and advance our understanding of adipose tissue plasticity and metabolic regulation in lampreys.

MobiChIP: a compatible library construction method of single-cell ChIP-seq based droplets

To illustrate epigenetic heterogeneity, versatile tools of single-cell ChIP-seq (scChIP-seq) are essential for both convenience and accuracy. We developed MobiChIP, a compatible ChIP-seq library construction method based on current sequencing platforms for single-cell applications. MobiChIP efficiently captures fragments from tagmented nuclei across various species and allows sample mixing from different tissues or species. This strategy offers robust nucleosome amplification and flexible sequencing without customized primers. MobiChIP reveals regulatory landscapes of chromatin with active (H3K27ac) and repressive (H3K27me3) histone modification in peripheral blood mononuclear cells (PBMCs) and accurately identifies epigenetic repression of the Hox gene cluster, outperforming ATAC-seq. Meanwhile, we also integrated scChIP-seq with scRNA-seq to further illustrate cellular genetic and epigenetic heterogeneity.

Single-cell sequencing in diabetic retinopathy: progress and prospects

Diabetic retinopathy is a major ocular complication of diabetes, characterized by progressive retinal microvascular damage and significant visual impairment in working-age adults. Traditional bulk RNA sequencing offers overall gene expression profiles but does not account for cellular heterogeneity. Single-cell RNA sequencing overcomes this limitation by providing transcriptomic data at the individual cell level and distinguishing novel cell subtypes, developmental trajectories, and intercellular communications. Researchers can use single-cell sequencing to draw retinal cell atlases and identify the transcriptomic features of retinal cells, enhancing our understanding of the pathogenesis and pathological changes in diabetic retinopathy. Additionally, single-cell sequencing is widely employed to analyze retinal organoids and single extracellular vesicles. Single-cell multi-omics sequencing integrates omics information, whereas stereo-sequencing analyzes gene expression and spatiotemporal data simultaneously. This review discusses the protocols of single-cell sequencing for obtaining single cells from retina and accurate sequencing data. It highlights the applications and advancements of single-cell sequencing in the study of normal retinas and the pathological changes associated with diabetic retinopathy. This underscores the potential of these technologies to deepen our understanding of the pathogenesis of diabetic retinopathy that may lead to the introduction of new therapeutic strategies.

Benchmarking cross-species single-cell RNA-seq data integration methods: towards a cell type tree of life

Cross-species single-cell RNA-seq data hold immense potential for unraveling cell type evolution and transferring knowledge between well-explored and less-studied species. However, challenges arise from interspecific genetic variation, batch effects stemming from experimental discrepancies and inherent individual biological differences. Here, we benchmarked nine data-integration methods across 20 species, encompassing 4.7 million cells, spanning eight phyla and the entire animal taxonomic hierarchy. Our evaluation reveals notable differences between the methods in removing batch effects and preserving biological variance across taxonomic distances. Methods that effectively leverage gene sequence information capture underlying biological variances, while generative model-based approaches excel in batch effect removal. SATURN demonstrates robust performance across diverse taxonomic levels, from cross-genus to cross-phylum, emphasizing its versatility. SAMap excels in integrating species beyond the cross-family level, especially for atlas-level cross-species integration, while scGen shines within or below the cross-class hierarchy. As a result, our analysis offers recommendations and guidelines for selecting suitable integration methods, enhancing cross-species single-cell RNA-seq analyses and advancing algorithm development.

Comparative evaluation of ACetic - MEthanol high salt dissociation approach for single-cell transcriptomics of frozen human tissues

Current dissociation methods for solid tissues in scRNA-seq studies do not guarantee intact single-cell isolation, especially for sensitive and complex human endocrine tissues. Most studies rely on enzymatic dissociation of fresh samples or nuclei isolation from frozen samples. Dissociating whole intact cells from fresh-frozen samples, commonly collected by biobanks, remains a challenge. Here, we utilized the acetic-methanol dissociation approach (ACME) to capture transcriptional profiles of individual cells from fresh-frozen tissue samples. This method combines acetic acid-based dissociation and methanol-based fixation. In our study, we optimized this approach for human endocrine tissue samples for the first time. We incorporated a high-salt washing buffer instead of the standard PBS to stabilize RNA and prevent RNases reactivation during rehydration. We have designated this optimized protocol as ACME HS (ACetic acid-MEthanol High Salt). This technique aims to preserve cell morphology and RNA integrity, minimizing transcriptome changes and providing a more accurate representation of mature mRNA. We compared the ability of enzymatic, ACME HS, and nuclei isolation methods to preserve major cell types, gene expression, and standard quality parameters across 41 tissue samples. Our results demonstrated that ACME HS effectively dissociates and fixes cells, preserving cell morphology and high RNA integrity. This makes ACME HS a valuable alternative for scRNA-seq protocols involving challenging tissues where obtaining a live cell suspension is difficult or disruptive.

xQTLatlas: a comprehensive resource for human cellular-resolution multi-omics genetic regulatory landscape

Understanding how genetic variants influence molecular phenotypes in different cellular contexts is crucial for elucidating the molecular and cellular mechanisms behind complex traits, which in turn has spurred significant advances in research into molecular quantitative trait locus (xQTL) at the cellular level. With the rapid proliferation of data, there is a critical need for a comprehensive and accessible platform to integrate this information. To meet this need, we developed xQTLatlas (http://www.hitxqtl.org.cn/), a database that provides a multi-omics genetic regulatory landscape at cellular resolution. xQTLatlas compiles xQTL summary statistics from 151 cell types and 339 cell states across 55 human tissues. It organizes these data into 20 xQTL types, based on four distinct discovery strategies, and spans 13 molecular phenotypes. Each entry in xQTLatlas is meticulously annotated with comprehensive metadata, including the origin of the tissue, cell type, cell state and the QTL discovery strategies utilized. Additionally, xQTLatlas features multiscale data exploration tools and a suite of interactive visualizations, facilitating in-depth analysis of cell-level xQTL. xQTLatlas provides a valuable resource for deepening our understanding of the impact of functional variants on molecular phenotypes in different cellular environments, thereby facilitating extensive research efforts.

A Dynamic Shift in Estrogen Receptor Expression During Granulosa Cell Differentiation in the Ovary

This study uncovers a dynamic shift in estrogen receptor expression during granulosa cell (GC) differentiation in the ovary, highlighting a transition from estrogen receptor alpha (ESR1) to estrogen receptor beta (ESR2). Using a transgenic mouse model with Esr1-iCre-mediated Esr2 deletion, we demonstrate that ESR2 expression is absent in GCs derived from ESR1-expressing ovarian surface epithelium (OSE) cells. Single-cell analysis of the OSE-GC lineage reveals a developmental trajectory from Esr1-expressing OSE cells to Foxl2-expressing pre-GCs, culminating in GCs exclusively expressing Esr2. Transcriptome analyses identified vasculature-derived TGFβ1 ligands as key regulators of this transition. Supporting this, TGFβ1 treatment of cultured embryonic ovaries reduced Esr1 expression while promoting Esr2 expression. This study underscores the capability of GCs to switch from ESR1 to ESR2 expression as a fundamental aspect of normal differentiation.

LnCeCell 2.0: an updated resource for lncRNA-associated ceRNA networks and web tools based on single-cell and spatial transcriptomics sequencing data

We describe LnCeCell 2.0 (http://bio-bigdata.hrbmu.edu.cn/LnCeCell), an updated resource for lncRNA-associated competing endogenous RNA (ceRNA) networks and web tools based on single-cell and spatial transcriptomics sequencing (stRNA-seq) data. We have updated the LnCeCell 2.0 database with significantly expanded data and improved features, including (i) 257 single-cell RNA sequencing and stRNA-seq datasets across 86 diseases/phenotypes and 80 human normal tissues, (ii) 836 581 cell-specific and spatial spot-specific ceRNA interactions and functional networks for 1 002 988 cells and 367 971 spatial spots, (iii) 15 489 experimentally supported lncRNA biomarkers related to disease pathology, diagnosis and treatment, (iv) detailed annotation of cell type, cell state, subcellular and extracellular locations of ceRNAs through manual curation and (v) ceRNA expression profiles and follow-up clinical information of 20 326 cancer patients. Further, a panel of 24 flexible tools (including 8 comprehensive and 16 mini-analysis tools) was developed to investigate ceRNA-regulated mechanisms at single-cell/spot resolution. The CeCellTraject tool, for example, illustrates the detailed ceRNA distribution of different cell populations and explores the dynamic change of the ceRNA network along the developmental trajectory. LnCeCell 2.0 will facilitate the study of fine-tuned lncRNA-ceRNA networks with single-cell and spatial spot resolution, helping us to understand the regulatory mechanisms behind complex microbial ecosystems.

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

Prognostic prediction and immune infiltration analysis based on lysosome and senescence state identifies MMP12 as a novel therapy target in gastric cancer

BACKGROUND AND AIMS

As humans undergo the aging process, they become more vulnerable to various types of cancers, including gastric cancer (GC), which is frequently associated with aging. The senescent phenotype is closely linked to lysosomes, but research on the combined impact of senescence and lysosomes on GC prognosis is scarce.

METHODS

To construct and validate a prognostic model for gastric cancer (GC), we obtained gene expression and clinical data of GC patients from Cancer Genome Atlas (TCGA) databases. We employed Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression for model construction and ConsensusClusterPlus R package for generating cluster heatmaps. The model's predictive ability was evaluated through Kaplan-Meier survival analysis and ROC curve analysis. Our analysis included an assessment of the senescence and lysosome state using expression profiles and immune infiltration analysis through CIBERSORT methods. Finally, we validated potential gene targets through cellular experiments.

RESULTS

"In this research, we discovered two subtypes of gastric cancer (GC), Cluster 1 and Cluster 2. These subtypes are characterized by the presence of lysosomes and senescence, and we have identified distinct molecular features unique to each subtype. We observed that Cluster 2 had a lower survival prognosis compared to Cluster 1. Additionally, we have developed a risk prediction model that takes into consideration the presence of lysosomes and senescence. Patients in the high-risk group, as predicted by our model, experienced shorter survival times. Further analysis included immune infiltration, immune checkpoint, and chemotherapy evaluation of GC patients. We have displayed the frequency of mutations and copy number variations (CNVs) in visual formats. Our cellular experiments demonstrated that the MMP12 gene serves as a protective factor in GC cells."

CONCLUSIONS

In conclusion, we have clarified the extensive relationship between lysosomes and senescence in GC and developed a risk signature to forecast the prognosis of GC patients. MMP12 could be a promising protective factor for GC patients and might present a novel concept for anticipating the efficacy of targeted therapies and immunotherapies in GC patients.

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.

HIPSD&R-seq enables scalable genomic copy number and transcriptome profiling

Single-cell DNA sequencing (scDNA-seq) enables decoding somatic cancer variation. Existing methods are hampered by low throughput or cannot be combined with transcriptome sequencing in the same cell. We propose HIPSD&R-seq (HIgh-throughPut Single-cell Dna and Rna-seq), a scalable yet simple and accessible assay to profile low-coverage DNA and RNA in thousands of cells in parallel. Our approach builds on a modification of the 10X Genomics platform for scATAC and multiome profiling. In applications to human cell models and primary tissue, we demonstrate the feasibility to detect rare clones and we combine the assay with combinatorial indexing to profile over 17,000 cells.

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.

The pan-cancer landscape of aldo-keto reductase1B10 reveals that its expression is diminished in gastric cancer

Introduction

Aldo-keto reductase 1B10 (AKR1B10) is a multifunctional enzyme, which is important in cancer development and progression, but the landscape of AKR1B10 in pan-cancers and in tumor microenvironment is unclear.

Method

This study integrated the sequencing data of 33 cancer types, including gastric cancer, from TCGA project to explored the expression pattern and genetic and epigenetic alterations of AKR1B10. The association of AKR1B10 expression with clinical progression of cancers was evaluated by Kaplan-Meier analysis; the potential role of AKR1B10 in tumor microenvironment (TME) and immune-related gene expression were analyzed by PURITY, ESTIMATE, TIMER and CIBERSORT algorithms. The expression of AKR1B10 and immune cell markers in gastric cancer were evaluated with multiplex immunofluorescence staining.

Result

Results indicated that AKR1B10 was highly expressed in the gastrointestinal tract in health donors, but the expression of AKR1B10 was significantly changed in most of cancer types, which may be ascribed to DNA methylation in its promoter. The AKR1B10 expression in cancers and its value in disease progression was bidirectional and functionally enriched in metabolism in pan-cancers. In tumor microenvironment, AKR1B10 was significantly correlated with immune cell infiltrations and immune gene expression. In the stomach, along with the diminishing of AKR1B10 expression, CD68+ macrophage increased and CD19+ B cell decreased in gastric cancer.

Discussion

These data indicates that AKR1B10 may be an important factor in the development and progression and a potential therapeutic target for multiple cancers, but plays as a protector in the gastric tissues.

CDK9 recruits HUWE1 to degrade RARα and offers therapeutic opportunities for cutaneous T-cell lymphoma

Cutaneous T-cell lymphoma (CTCL) is a heterogeneous non-Hodgkin lymphoma originating in the skin and invading the systemic hematopoietic system. Current treatments, including chemotherapy and monoclonal antibodies yielded limited responses with high incidence of side effects, highlighting the need for targeted therapy. Screening with small inhibitors library, herein we identify cyclin dependent kinase 9 (CDK9) as a driver of CTCL growth. Single-cell RNA-seq analysis reveals a CDK9high malignant T cell cluster with a unique actively proliferating feature. Inhibition, depletion or proteolysis targeting chimera (PROTAC)-mediated degradation of CDK9 significantly reduces CTCL cell growth in vitro and in murine models. CDK9 also promotes degradation of retinoic acid receptor α (RARα) via recruiting the E3 ligase HUWE1. Co-administration of CDK9-PROTAC (GT-02897) with all-trans retinoic acid (ATRA) leads to synergistic attenuation of tumor growth in vitro and in xenograft models, providing a potential translational treatment for complete eradication of CTCL.

Transcriptomic Analysis of Air-Liquid Interface Culture in Human Lung Organoids Reveals Regulators of Epithelial Differentiation

To develop in vitro respiratory models, it is crucial to identify the factors involved in epithelial cell differentiation. In this study, we comprehensively analyzed the effects of air-liquid interface (ALI) culture on epithelial cell differentiation using single-cell RNA sequencing (scRNA-seq). ALI culture induced a pronounced shift in cell composition, marked by a fivefold increase in ciliated cells and a reduction of more than half in basal cells. Transcriptional signatures associated with epithelial cell differentiation, analyzed using iPathwayGuide software, revealed the downregulation of VEGFA and upregulation of CDKN1A as key signals for epithelial differentiation. Our findings highlight the efficacy of the ALI culture for replicating the human lung airway epithelium and provide valuable insights into the crucial factors that influence human ciliated cell differentiation.

Cross-species single-cell analysis reveals divergence and conservation of peripheral blood mononuclear cells

BACKGROUND

Single-cell transcriptome sequencing (scRNA-seq) has revolutionized the study of immune cells by overcoming the limitations of traditional antibody-based identification and isolation methods. This advancement allows us to obtain comprehensive gene expression profiles from a diverse array of vertebrate species, facilitating the identification of various cell types. Comparative immunology across vertebrates presents a promising approach to understanding the evolution of immune cell types. In this study, we conducted a comparative transcriptome analysis of peripheral blood mononuclear cells (PBMCs) at the single-cell level across 12 species.

RESULTS

Our findings shed light on the cellular compositional features of PBMCs, spanning from fish to mammals. Notably, we identified genes that exhibit vertebrate universality in characterizing immune cells. Moreover, our investigation revealed that monocytes have maintained a conserved transcriptional regulatory program throughout evolution, emphasizing their pivotal role in orchestrating immune cells to execute immune programs.

CONCLUSIONS

This comprehensive analysis provides valuable insights into the evolution of immune cells across vertebrates.

Probe set selection for targeted spatial transcriptomics

Targeted spatial transcriptomic methods capture the topology of cell types and states in tissues at single-cell and subcellular resolution by measuring the expression of a predefined set of genes. The selection of an optimal set of probed genes is crucial for capturing the spatial signals present in a tissue. This requires selecting the most informative, yet minimal, set of genes to profile (gene set selection) for which it is possible to build probes (probe design). However, current selections often rely on marker genes, precluding them from detecting continuous spatial signals or new states. We present Spapros, an end-to-end probe set selection pipeline that optimizes both gene set specificity for cell type identification and within-cell type expression variation to resolve spatially distinct populations while considering prior knowledge as well as probe design and expression constraints. We evaluated Spapros and show that it outperforms other selection approaches in both cell type recovery and recovering expression variation beyond cell types. Furthermore, we used Spapros to design a single-cell resolution in situ hybridization on tissues (SCRINSHOT) experiment of adult lung tissue to demonstrate how probes selected with Spapros identify cell types of interest and detect spatial variation even within cell types.

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.

scRGCL: a cell type annotation method for single-cell RNA-seq data using residual graph convolutional neural network with contrastive learning

Cell type annotation is a critical step in analyzing single-cell RNA sequencing (scRNA-seq) data. A large number of deep learning (DL)-based methods have been proposed to annotate cell types of scRNA-seq data and have achieved impressive results. However, there are several limitations to these methods. First, they do not fully exploit cell-to-cell differential features. Second, they are developed based on shallow features and lack of flexibility in integrating high-order features in the data. Finally, the low-dimensional gene features may lead to overfitting in neural networks. To overcome those limitations, we propose a novel DL-based model, cell type annotation of single-cell RNA-seq data using residual graph convolutional neural network with contrastive learning (scRGCL), based on residual graph convolutional neural network and contrastive learning for cell type annotation of single-cell RNA-seq data. scRGCL mainly consists of a residual graph convolutional neural network, contrastive learning, and weight freezing. A residual graph convolutional neural network is utilized to extract complex high-order features from data. Contrastive learning can help the model learn meaningful cell-to-cell differential features. Weight freezing can avoid overfitting and help the model discover the impact of specific gene expression on cell type annotation. To verify the effectiveness of scRGCL, we compared its performance with six methods (three shallow learning algorithms and three state-of-the-art DL-based methods) on eight single-cell benchmark datasets from two species (seven in human and one in mouse). Experimental results not only show that scRGCL outperforms competing methods but also demonstrate the generalizability of scRGCL for cell type annotation. scRGCL is available at https://github.com/nathanyl/scRGCL.

Senescent T Cells: The Silent Culprit in Acute Myeloid Leukemia Progression?

Malignant tumors can evade immune surveillance and elimination through multiple mechanisms, with the induction of immune cell dysfunction serving as a crucial strategy. Mounting evidence indicates that T cell senescence constitutes the primary mechanism underlying T cell dysfunction in acute myeloid leukemia (AML) and represents one of the potential causes of immunotherapy failure. AML usually progresses rapidly and is highly susceptible to drug resistance, thereby resulting in recurrence and patient mortality. Hence, disrupting the immune interface within the bone marrow microenvironment of AML has emerged as a critical objective for synergistically enhancing tumor immunotherapy. In this review, we summarize the general characteristics, distinctive phenotypes, and regulatory signaling networks of senescent T cells and highlight their potential clinical significance in the bone marrow microenvironment of AML. Additionally, we discuss potential therapeutic strategies for alleviating and reversing T cell senescence.

Multi-omics analysis deciphers intercellular communication regulating oxidative stress to promote oral squamous cell carcinoma progression

Oral squamous cell carcinoma (OSCC) is a common malignant tumor in the head and neck, associated with high recurrence and poor prognosis. We performed an integrated analysis of single-cell RNA and spatial transcriptomic data from cancerous and normal tissues to create a comprehensive atlas of epithelial cells and cancer-associated fibroblasts (CAFs). Our findings show that AKR1C3+ epithelial cells, located at the tumor's stromal front, exhibit significant copy number variation and poor prognostic indicators, suggesting a role in tumor invasion. We also identified a distinct group of early-stage CAFs (named OSCC_Normal, characterized by ADH1B+, MFAP4+, and PLA2G2A+) that interact with AKR1C3+ cells, where OSCC_Normal may inhibit the FOXO1 redox switch in these epithelial cells via the IGF1/IGF1R pathway, causing oxidative stress overload. Conversely, AKR1C3+ cells use ITGA6/ITGB4 receptor to counteract the effects of OSCC_Normal, promoting cancer invasion. This study unveils complex interactions within the OSCC tumor microenvironment.

Lensless On-Chip Chemiluminescence Imaging for High-Throughput Single-Cell Heterogeneity Analysis

High-throughput single-cell heterogeneity imaging and analysis is essential for understanding complex biological systems and for advancing personalized precision disease diagnosis and treatment. Here, we present a miniaturized lensless chemiluminescence chip for high-throughput single-cell functional imaging with subcellular resolution. With the sensitive chemiluminescence sensing and wide field of view of contact lensless imaging, we demonstrated the chemiluminescent imaging of over 1000 single cells, and their membrane glycoprotein and the high-throughput single-cell heterogeneity of membrane protein imaging were examined for precision analysis. Furthermore, the functional adhesion and heterogeneity of single live cells were imaged and explored. This miniaturized lensless on-chip CL-CMOS imaging platform enables high-throughput single-cell imaging and analysis with high sensitivity and subcellular resolution, providing new techniques for the cellular study of biological heterogeneity and has potential application in precision disease diagnosis and treatment at the point-of-care settings.

Clustering-independent estimation of cell abundances in bulk tissues using single-cell RNA-seq data

Single-cell RNA sequencing has transformed the study of biological tissues by enabling transcriptomic characterizations of their constituent cell states. Computational methods for gene expression deconvolution use this information to infer the cell composition of related tissues profiled at the bulk level. However, current deconvolution methods are restricted to discrete cell types and have limited power to make inferences about continuous cellular processes such as cell differentiation or immune cell activation. We present ConDecon, a clustering-independent method for inferring the likelihood for each cell in a single-cell dataset to be present in a bulk tissue. ConDecon represents an improvement in phenotypic resolution and functionality with respect to regression-based methods. Using ConDecon, we discover the implication of neurodegenerative microglia inflammatory pathways in the mesenchymal transformation of pediatric ependymoma and characterize their spatial trajectories of activation. The generality of this approach enables the deconvolution of other data modalities, such as bulk ATAC-seq data.

Single-cell sequencing: Current applications in various tuberculosis specimen types

Tuberculosis (TB) is a chronic disease caused by Mycobacterium tuberculosis (M.tb) and responsible for millions of deaths worldwide each year. It has a complex pathogenesis that primarily affects the lungs but can also impact systemic organs. In recent years, single-cell sequencing technology has been utilized to characterize the composition and proportion of immune cell subpopulations associated with the pathogenesis of TB disease since it has a high resolution that surpasses conventional techniques. This paper reviews the current use of single-cell sequencing technologies in TB research and their application in analysing specimens from various sources of TB, primarily peripheral blood and lung specimens. The focus is on how these technologies can reveal dynamic changes in immune cell subpopulations, genes and proteins during disease progression after M.tb infection. Based on the current findings, single-cell sequencing has significant potential clinical value in the field of TB research. Next, we will focus on the real-world applications of the potential targets identified through single-cell sequencing for diagnostics, therapeutics and the development of effective vaccines.

Sex-Based Differences in Thyroid Plasma B Cell Infiltration: Implications for Autoimmune Disease Susceptibility

Thyroid autoimmune diseases, such as Hashimoto thyroiditis and Graves disease, are significantly more prevalent in women than in men, suggesting underlying biological differences in immune system function and regulation between sexes. Plasma B cells are crucial in autoimmunity due to their role in producing antibodies targeting self-antigens, but their presence in the thyroids of women without clinical autoimmune diseases remains largely unexplored. This study investigates the infiltration of plasma B cells in female thyroids specifically excluding those with any clinical signs of autoimmune diseases. Using bulk RNA-seq analysis, we identified significant sex differences in gene expression profiles, particularly in genes associated with plasma B cells. Single-cell RNA-seq and spatial transcriptomic analyses further revealed that the CXCL13-CXCR5 signaling axis plays a pivotal role in recruiting and organizing plasma B cells within the thyroid tissue. These findings suggest that the inherent presence of plasma B cells in the female thyroid, driven by CXCL13, may contribute to the higher risk of developing autoimmune thyroid diseases in women. Our study provides new insights into the immune landscape of the thyroid and underscores the importance of understanding sex-specific differences in immune cell distribution and function.

Developmental-status-aware transcriptional decomposition establishes a cell state panorama of human cancers

BACKGROUND

Cancer cells evolve under unique functional adaptations that unlock transcriptional programs embedded in adult stem and progenitor-like cells for progression, metastasis, and therapeutic resistance. However, it remains challenging to quantify the stemness-aware cell state of a tumor based on its gene expression profile.

METHODS

We develop a developmental-status-aware transcriptional decomposition strategy using single-cell RNA-sequencing-derived tissue-specific fetal and adult cell signatures as anchors. We apply our method to various biological contexts, including developing human organs, adult human tissues, experimentally induced differentiation cultures, and bulk human tumors, to benchmark its performance and to reveal novel biology of entangled developmental signaling in oncogenic processes.

RESULTS

Our strategy successfully captures complex dynamics in developmental tissue bulks, reveals remarkable cellular heterogeneity in adult tissues, and resolves the ambiguity of cell identities in in vitro transformations. Applying it to large patient cohorts of bulk RNA-seq, we identify clinically relevant cell-of-origin patterns and observe that decomposed fetal cell signals significantly increase in tumors versus normal tissues and metastases versus primary tumors. Across cancer types, the inferred fetal-state strength outperforms published stemness indices in predicting patient survival and confers substantially improved predictive power for therapeutic responses.

CONCLUSIONS

Our study not only provides a general approach to quantifying developmental-status-aware cell states of bulk samples but also constructs an information-rich, biologically interpretable, cell-state panorama of human cancers, enabling diverse translational applications.