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Harmonized cross-species cell atlases of trigeminal and dorsal root ganglia.
Peripheral sensory neurons in the dorsal root ganglion (DRG) and trigeminal ganglion (TG) are specialized to detect and transduce diverse environmental stimuli including touch, temperature, and pain to the central nervous system. Recent advances in single-cell RNA-sequencing (scRNA-seq) have provided new insights into the diversity of sensory ganglia cell types in rodents, non-human primates, and humans, but it remains difficult to compare transcriptomically defined cell types across studies and species. Here, we built cross-species harmonized atlases of DRG and TG cell types that describe 18 neuronal and 11 non-neuronal cell types across 6 species and 19 studies. We then demonstrate the utility of this harmonized reference atlas by using it to annotate newly profiled DRG nuclei/cells from both human and the highly regenerative axolotl. We observe that the transcriptomic profiles of sensory neuron subtypes are broadly similar across vertebrates, but the expression of functionally important neuropeptides and channels can vary notably. The new resources and data presented here can guide future studies in comparative transcriptomics, simplify cell type nomenclature differences across studies, and help prioritize targets for future pain therapy development.
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Tumor-Infiltrating PD-1hiCD8+-T-Cell Signature as an Effective Biomarker for Immune Checkpoint Inhibitor Therapy Response Across Multiple Cancers.
BACKGROUND: Stratification of patients who could benefit from immune checkpoint inhibitor (ICI) therapy is of much importance. PD-1hiCD8+ T cells represent a newly identified and effective biomarker for ICI therapy response biomarker in lung cancer. Accurately quantifying these T cells using commonly available RNA sequencing (RNA-seq) data may extend their applications to more cancer types. METHOD: We built a transcriptome signature of PD-1hiCD8+ T cells from bulk RNA-seq and single-cell RNA-seq (scRNA-seq) data of tumor-infiltrating immune cells. The signature was validated by flow cytometry and in independent datasets. The clinical applications of the signature were explored in non-small-cell lung cancer, melanoma, gastric cancer, urothelial cancer, and a mouse model of breast cancer samples treated with ICI, and systematically evaluated across 21 cancer types in The Cancer Genome Atlas (TCGA). Its associations with other biomarkers were also determined. RESULTS: Signature scores could be used to identify the PD-1hiCD8+ T subset and were correlated with the fraction of PD-1hiCD8+ T cells in tumor tissue (Pearson correlation, R=0.76, p=0.0004). Furthermore, in the scRNA-seq dataset, we confirmed the capability of PD-1hiCD8+ T cells to secrete CXCL13, as well as their interactions with other immune cells. In 581 clinical samples and 204 mouse models treated with ICIs, high signature scores were associated with increased survival, and the signature achieved area under the receiver operating characteristic curve scores of 0.755 (ranging from 0.61 to 0.91) in predicting therapy response. In TCGA pan-cancer datasets, our signature scores were consistently correlated with therapy response (R=0.78, p<0.0001) and partially explained the diverse response rates among different cancer types. Finally, our signature generally outperformed other mRNA-based predictors and showed improved predictive performance when used in combination with tumor mutational burden (TMB). The signature score is available in the R package "PD1highCD8Tscore" (https://github.com/Liulab/PD1highCD8Tscore). CONCLUSION: Through estimating the fraction of the PD-1hiCD8+ T cell, our signature could predict response to ICI therapy across multiple cancers and could serve as a complementary biomarker to TMB.
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SHMT2: a Metabolic and Immune Biomarker of Aggressive Lung Adenocarcinoma.
Serine/glycine-one-carbon (SGOC) metabolism is frequently altered in lung adenocarcinoma (LUAD), but its relationship to tumor behavior and predicted immunotherapy responsiveness remains incompletely defined. Metabolomic profiling of 23 paired LUAD and adjacent normal lung tissues was performed using internal extractive electrospray ionization mass spectrometry. Transcriptomic and clinical data from The Cancer Genome Atlas LUAD cohort (TCGA-LUAD) were analyzed to assess SHMT2 expression, prognosis, differentially expressed genes, and immune-related features. Predicted response to immune checkpoint blockade was evaluated using Tumor Immune Dysfunction and Exclusion (TIDE) and The Cancer Immunome Atlas (TCIA), and drug sensitivity was inferred using oncoPredict. Single-cell RNA-seq data were used to examine the cellular distribution of SHMT2. Experimental validation included quantitative reverse-transcription PCR (RT-qPCR), western blotting, Human Protein Atlas (HPA) immunohistochemistry, and short hairpin RNA (shRNA)-mediated SHMT2 knockdown followed by proliferation, wound-healing and colony formation assays. Metabolomic analysis identified glycine, serine, and threonine metabolism as a prominently altered pathway in LUAD. SHMT2 was upregulated in LUAD and associated with worse overall survival and adverse clinicopathological features. SHMT2-high tumors displayed enrichment of cell-cycle and SGOC-related transcriptional programs, lower immune and stromal scores, and reduced predicted responsiveness to immunotherapy. Single-cell analysis showed relative enrichment of SHMT2 expression in B cell populations. In vitro, SHMT2 was overexpressed in LUAD cells, and its knockdown suppressed proliferation, migration, and clonogenic growth. Collectively, SHMT2 is associated with SGOC metabolic reprogramming, aggressive tumor phenotypes, and an immune-disadvantaged state in LUAD, supporting its potential relevance as a biomarker; therapeutic targeting requires additional pharmacologic and in vivo validation.
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Cardiac SARS-CoV-2 infection is associated with pro-inflammatory transcriptomic alterations within the heart.
AIMS: Cardiac involvement in COVID-19 is associated with adverse outcome. However, it is unclear whether cell-specific consequences are associated with cardiac SARS-CoV-2 infection. Therefore, we investigated heart tissue utilizing in situ hybridization, immunohistochemistry, and RNA-sequencing in consecutive autopsy cases to quantify virus load and characterize cardiac involvement in COVID-19. METHODS AND RESULTS: In this study, 95 SARS-CoV-2-positive autopsy cases were included. A relevant SARS-CoV-2 virus load in the cardiac tissue was detected in 41/95 deceased (43%). Massive analysis of cDNA ends (MACE)-RNA-sequencing was performed to identify molecular pathomechanisms caused by the infection of the heart. A signature matrix was generated based on the single-cell dataset 'Heart Cell Atlas' and used for digital cytometry on the MACE-RNA-sequencing data. Thus, immune cell fractions were estimated and revealed no difference in immune cell numbers in cases with and without cardiac infection. This result was confirmed by quantitative immunohistological diagnosis. MACE-RNA-sequencing revealed 19 differentially expressed genes (DEGs) with a q-value <0.05 (e.g. up: IFI44L, IFT3, TRIM25; down: NPPB, MB, MYPN). The upregulated DEGs were linked to interferon pathways and originate predominantly from endothelial cells. In contrast, the downregulated DEGs originate predominately from cardiomyocytes. Immunofluorescent staining showed viral protein in cells positive for the endothelial marker ICAM1 but rarely in cardiomyocytes. The Gene Ontology (GO) term analysis revealed that downregulated GO terms were linked to cardiomyocyte structure, whereas upregulated GO terms were linked to anti-virus immune response. CONCLUSION: This study reveals that cardiac infection induced transcriptomic alterations mainly linked to immune response and destruction of cardiomyocytes. While endothelial cells are primarily targeted by the virus, we suggest cardiomyocyte destruction by paracrine effects. Increased pro-inflammatory gene expression was detected in SARS-CoV-2-infected cardiac tissue but no increased SARS-CoV-2 associated immune cell infiltration was observed.
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An age-and sex-matched postoperative therapy should be required in thyroid papillary carcinoma.
BACKGROUND AND PURPOSE: Thyroid papillary carcinoma (PTC) had a high possibility of recurrence after surgery, and thyroid stimulating hormone (TSH) suppression and radioactive iodine (131I) were used for postoperative therapy. This study explored the potential mechanism of lymph node metastasis (LNM) and aimed to develop differentiated treatments for PTC. METHOD: This study explored the risk factors of lymph node metastasis in PTC by analyzing the clinical information of 2073 cases. The Cancer Genome Atlas Thyroid Cancer (TCGA-THCA) and the Gene Expression Omnibus (GEO) databases of gene expression were analyzed to identify the interrelationships between gene expression to phenotype. RESULTS: Analyzing clinical data, we found that male gender, younger age, larger tumor size, and extra-thyroidal extension (ETE) were risk significant risk factors for lymph node metastasis(P<0.05). Conversely, thyroid function parameters such as TSH, FT3, FT4, TSH/FT3, and TSH/FT4 didn't correlate with LNM(P>0.05), and TSH levels were observed to be higher in females(P<0.05). Gene expression analysis revealed that SLC5A5 was down-regulated in males, younger individuals, and those with lymph node metastasis, and a lower level of SLC5A5 was associated with a worse disease-free survival(P<0.05). Additionally, our examination of single-cell RNA sequencing (scRNA-seq) data indicated that SLC5A5 expression was reduced in tumors and lymph node metastasis samples, correlating positively with the expression of TSHR. CONCLUSION: The impact of TSH on PTC behavior remained unclear, while the capacity for absorbing 131I in dependence on SLC5A5 showed variations across different genders and ages. We conclude that postoperative treatment of PTC should take into account the differences caused by gender and age.
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SNAI1 promotes epithelial-mesenchymal transition and maintains cancer stem cell-like properties in thymic epithelial tumors through the PIK3R2/p-EphA2 Axis.
BACKGROUND: Thymic epithelial tumors (TETs) are infrequent malignancies that arise from the anterior mediastinum. Therapeutic options for TETs, especially thymic carcinoma (TC), remain relatively constrained. This study aims to investigate the oncogenic hub gene and its underlying mechanisms in TETs, as well as to identify potential therapeutic targets. METHODS: Weighted gene co-expression network analysis (WGCNA) and differential gene expression (DEG) analysis were utilized to identify significant oncogenes using The Cancer Genome Atlas (TCGA) database. LASSO logistic regression analysis was performed to assess the association between hub genes and clinical parameters. The influence of the hub gene on promoting epithelial-mesenchymal transition (EMT), tumor progression, and regulating cancer stem cell-like properties was assessed both in vitro and in vivo. Single-cell RNA sequencing (scRNA-seq) was utilized to analyze the alterations in the tumor and its microenvironment following the administration of the hub gene's inhibitor. Multiplex immunohistochemistry (mIHC) was employed to validate the results. The potential mechanism was further elucidated through the utilization of Cleavage Under Targets and Tagmentation (CUT&Tag), RNA-sequencing, chromatin immunoprecipitation (ChIP), CUT&RUN, luciferase reporter assay, co-immunoprecipitation (Co-IP), mass spectrometry (MS) and phosphoproteomic assays. RESULTS: SNAI1 was identified as a hub transcription factor for TETs, and its positive correlation with the invasiveness of the disease was confirmed. Subsequent experiments revealed that the upregulation of SNAI1 augmented the migration, invasion, and EMT of TET cell lines. Furthermore, we observed that the overexpression of SNAI1 sustained cancer stem cell-like properties. ScRNA-seq demonstrated that the use of a SNAI1 inhibitor inhibited the transition of macrophages from M1 to M2 phenotype, a finding further validated by multiplex immunohistochemistry (mIHC). Phosphoinositide-3-kinase regulatory subunit 2 (PIK3R2) was identified as one of the downstream targets of SNAI1 through CUT&Tag and RNA-sequencing, a finding validated by ChIP-qPCR, CUT&RUN-qPCR, luciferase reporter and immunofluorescence assays. Co-IP, MS and phosphoproteomic assays further confirmed that PIK3R2 directly interacted with phosphorylated EphA2 (p-EphA2), facilitating downstream GSK3β/β-catenin signaling pathway. CONCLUSION: The tumorigenic role of SNAI1 through the PIK3R2/p-EphA2 axis was preliminarily validated in TETs. A potential therapeutic strategy for TETs may involve the inhibition of SNAI1.
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BrainSTEM: A single-cell multiresolution fetal brain atlas reveals transcriptomic fidelity of human midbrain cultures.
Protocols for deriving midbrain dopaminergic (mDA) neurons for Parkinson's disease (PD) modeling and therapy remain incompletely benchmarked against in vivo references. To establish transcriptomic standards, we generated an integrated human fetal whole-brain atlas and a midbrain subatlas. Whole-brain analysis revealed strong region-specific signatures, underscoring the need for global mapping before refined midbrain annotation. We implemented this two-tier strategy, BrainSTEM (Brain Single-cell Two tiEr Mapping), to systematically reassess published single-cell datasets of human midbrain culture models. BrainSTEM confirmed the presence of bona fide midbrain cell types ("on-target"), but also revealed substantial populations aligning with nonmidbrain regions ("off-target"), inflating reported mDA yields across protocols. This unbiased framework enables rigorous evaluation of differentiation outcomes, clarifies current limitations of midbrain-directed models, and provides a foundation for refining protocols toward more faithful in vitro systems for PD research and regenerative applications.
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A simple, scalable approach to building a cross-platform transcriptome atlas.
Gene expression atlases have transformed our understanding of the development, composition and function of human tissues. New technologies promise improved cellular or molecular resolution, and have led to the identification of new cell types, or better defined cell states. But as new technologies emerge, information derived on old platforms becomes obsolete. We demonstrate that it is possible to combine a large number of different profiling experiments summarised from dozens of laboratories and representing hundreds of donors, to create an integrated molecular map of human tissue. As an example, we combine 850 samples from 38 platforms to build an integrated atlas of human blood cells. We achieve robust and unbiased cell type clustering using a variance partitioning method, selecting genes with low platform bias relative to biological variation. Other than an initial rescaling, no other transformation to the primary data is applied through batch correction or renormalisation. Additional data, including single-cell datasets, can be projected for comparison, classification and annotation. The resulting atlas provides a multi-scaled approach to visualise and analyse the relationships between sets of genes and blood cell lineages, including the maturation and activation of leukocytes in vivo and in vitro. In allowing for data integration across hundreds of studies, we address a key reproduciblity challenge which is faced by any new technology. This allows us to draw on the deep phenotypes and functional annotations that accompany traditional profiling methods, and provide important context to the high cellular resolution of single cell profiling. Here, we have implemented the blood atlas in the open access Stemformatics.org platform, drawing on its extensive collection of curated transcriptome data. The method is simple, scalable and amenable for rapid deployment in other biological systems or computational workflows.
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Multimodal single-cell and whole-genome sequencing of small, frozen clinical specimens.
Single-cell genomics enables dissection of tumor heterogeneity and molecular underpinnings of drug response at an unprecedented resolution1-11. However, broad clinical application of these methods remains challenging, due to several practical and preanalytical challenges that are incompatible with typical clinical care workflows, namely the need for relatively large, fresh tissue inputs. In the present study, we show that multimodal, single-nucleus (sn)RNA/T cell receptor (TCR) sequencing, spatial transcriptomics and whole-genome sequencing (WGS) are feasible from small, frozen tissues that approximate routinely collected clinical specimens (for example, core needle biopsies). Compared with data from sample-matched fresh tissue, we find a similar quality in the biological outputs of snRNA/TCR-seq data, while reducing artifactual signals and compositional biases introduced by fresh tissue processing. Profiling sequentially collected melanoma samples from a patient treated in the KEYNOTE-001 trial12, we resolved cellular, genomic, spatial and clonotype dynamics that represent molecular patterns of heterogeneous intralesional evolution during anti-programmed cell death protein 1 therapy. To demonstrate applicability to banked biospecimens of rare diseases13, we generated a single-cell atlas of uveal melanoma liver metastasis with matched WGS data. These results show that single-cell genomics from archival, clinical specimens is feasible and provides a framework for translating these methods more broadly to the clinical arena.
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Identification of cell types in a mouse brain single-cell atlas using low sampling coverage.
BACKGROUND: High throughput methods for profiling the transcriptomes of single cells have recently emerged as transformative approaches for large-scale population surveys of cellular diversity in heterogeneous primary tissues. However, the efficient generation of such atlases will depend on sufficient sampling of diverse cell types while remaining cost-effective to enable a comprehensive examination of organs, developmental stages, and individuals. RESULTS: To examine the relationship between sampled cell numbers and transcriptional heterogeneity in the context of unbiased cell type classification, we explored the population structure of a publicly available 1.3 million cell dataset from E18.5 mouse brain and validated our findings in published data from adult mice. We propose a computational framework for inferring the saturation point of cluster discovery in a single-cell mRNA-seq experiment, centered around cluster preservation in downsampled datasets. In addition, we introduce a "complexity index," which characterizes the heterogeneity of cells in a given dataset. Using Cajal-Retzius cells as an example of a limited complexity dataset, we explored whether the detected biological distinctions relate to technical clustering. Surprisingly, we found that clustering distinctions carrying biologically interpretable meaning are achieved with far fewer cells than the originally sampled, though technical saturation of rare populations such as Cajal-Retzius cells is not achieved. We additionally validated these findings with a recently published atlas of cell types across mouse organs and again find using subsampling that a much smaller number of cells recapitulates the cluster distinctions of the complete dataset. CONCLUSIONS: Together, these findings suggest that most of the biologically interpretable cell types from the 1.3 million cell database can be recapitulated by analyzing 50,000 randomly selected cells, indicating that instead of profiling few individuals at high "cellular coverage," cell atlas studies may instead benefit from profiling more individuals, or many time points at lower cellular coverage and then further enriching for populations of interest. This strategy is ideal for scenarios where cost and time are limited, though extremely rare populations of interest (< 1%) may be identifiable only with much higher cell numbers.