A switch in the pathway of TRPC3-mediated calcium influx into brain pericytes contributes to capillary spasms after subarachnoid hemorrhage.

Calcium influx and subsequent elevation of the intracellular calcium concentration ([Ca2+]i) induce contractions of brain pericytes and capillary spasms following subarachnoid hemorrhage. This calcium influx is exerted through cation channels. However, the specific calcium influx pathways in brain pericytes after subarachnoid hemorrhage remain unknown. Transient receptor potential canonical 3 (TRPC3) is the most abundant cation channel potentially involved in calcium influx into brain pericytes and is involved in calcium influx into other cell types either via store-operated calcium entry (SOCE) or receptor-operated calcium entry (ROCE). Therefore, we hypothesized that TRPC3 is associated with [Ca2+]i elevation in brain pericytes, potentially mediating brain pericyte contraction and capillary spasms after subarachnoid hemorrhage. In this study, we isolated rat brain pericytes and demonstrated increased TRPC3 expression and its currents in brain pericytes after subarachnoid hemorrhage. Calcium imaging of brain pericytes revealed that changes in TRPC3 expression mediated a switch from SOCE-dominant to ROCE-dominant calcium influx after subarachnoid hemorrhage, resulting in significantly higher [Ca2+]i levels after SAH. TRPC3 activity in brain pericytes also contributed to capillary spasms and reduction in cerebral blood flow in an in vivo rat model of subarachnoid hemorrhage. Therefore, we suggest that the switch in TRPC3-mediated calcium influx pathways plays a crucial role in the [Ca2+]i elevation in brain pericytes after subarachnoid hemorrhage, ultimately leading to capillary spasms and a reduction in cerebral blood flow.

Predicting prognosis and immunotherapy response in colorectal cancer by pericytes insights from single-cell RNA sequencing.

Immunotherapy has revolutionized the treatment of tumors, but there are still a large number of patients who do not benefit from immunotherapy. Pericytes play an important role in remodeling the immune microenvironment. However, how pericytes affect the prognosis and treatment resistance of tumors is still unknown. This study jointly analyzed single-cell RNA sequencing (scRNA-seq) data and bulk RNA sequencing data of multiple cancers to reveal pericyte function in the colorectal cancer microenvironment. Analyzing over 800 000 cells, it was found that colorectal cancer had more pericyte enrichment in tumor tissues than other cancers. We then combined the TCGA database with multiple public datasets and enrolled more than 1000 samples, finding that pericyte may be closely related to poor prognosis due to the higher epithelial-mesenchymal transition (EMT) and hypoxic characteristics. At the same time, patients with more pericytes have higher immune checkpoint molecule expressions and lower immune cell infiltration. Finally, the contributions of pericyte in poor treatment response have been demonstrated in multiple immunotherapy datasets (n = 453). All of these observations suggest that pericyte can be used as a potential biomarker to predict patient disease progression and immunotherapy response.

Imaging Vital and Non-vital Brain Pericytes in Brain Slices following Subarachnoid Hemorrhage.

Pericytes are crucial mural cells situated within cerebral microcirculation, pivotal in actively modulating cerebral blood flow via contractility adjustments. Conventionally, their contractility is gauged by observing morphological shifts and nearby capillary diameter changes under specific circumstances. Yet, post-tissue fixation, evaluating vitality and ensuing pericyte contractility of imaged brain pericytes becomes compromised. Similarly, genetically labeling brain pericytes falls short in distinguishing between viable and non-viable pericytes, particularly in neurologic conditions like subarachnoid hemorrhage (SAH), where our preliminary investigation validates brain pericyte demise. A reliable protocol has been devised to surmount these constraints, enabling simultaneous fluorescent tagging of both functional and non-functional brain pericytes in brain sections. This labeling method allows high-resolution confocal microscope visualization, concurrently marking the brain slice microvasculature. This innovative protocol offers a means to appraise brain pericyte contractility, its impact on capillary diameter, and pericyte structure. Investigating brain pericyte contractility within the SAH context yields insightful comprehension of its effects on cerebral microcirculation.

The tumor immune microenvironment of nasopharyngeal carcinoma after gemcitabine plus cisplatin treatment.

Gemcitabine plus cisplatin (GP) chemotherapy is the standard of care for nasopharyngeal carcinoma (NPC). However, the mechanisms underpinning its clinical activity are unclear. Here, using single-cell RNA sequencing and T cell and B cell receptor sequencing of matched, treatment-naive and post-GP chemotherapy NPC samples (n = 15 pairs), we show that GP chemotherapy activated an innate-like B cell (ILB)-dominant antitumor immune response. DNA fragments induced by chemotherapy activated the STING type-I-interferon-dependent pathway to increase major histocompatibility complex class I expression in cancer cells, and simultaneously induced ILB via Toll-like receptor 9 signaling. ILB further expanded follicular helper and helper type 1 T cells via the ICOSL-ICOS axis and subsequently enhanced cytotoxic T cells in tertiary lymphoid organ-like structures after chemotherapy that were deficient for germinal centers. ILB frequency was positively associated with overall and disease-free survival in a phase 3 trial of patients with NPC receiving GP chemotherapy ( NCT01872962 , n = 139). It also served as a predictor for favorable outcomes in patients with NPC treated with GP and immunotherapy combined treatment (n = 380). Collectively, our study provides a high-resolution map of the tumor immune microenvironment after GP chemotherapy and uncovers a role for B cell-centered antitumor immunity. We also identify and validate ILB as a potential biomarker for GP-based treatment in NPC, which could improve patient management.

Macrophages promote anti-androgen resistance in prostate cancer bone disease.

Metastatic castration-resistant prostate cancer (PC) is the final stage of PC that acquires resistance to androgen deprivation therapies (ADT). Despite progresses in understanding of disease mechanisms, the specific contribution of the metastatic microenvironment to ADT resistance remains largely unknown. The current study identified that the macrophage is the major microenvironmental component of bone-metastatic PC in patients. Using a novel in vivo model, we demonstrated that macrophages were critical for enzalutamide resistance through induction of a wound-healing-like response of ECM-receptor gene expression. Mechanistically, macrophages drove resistance through cytokine activin A that induced fibronectin (FN1)-integrin alpha 5 (ITGA5)-tyrosine kinase Src (SRC) signaling cascade in PC cells. This novel mechanism was strongly supported by bioinformatics analysis of patient transcriptomics datasets. Furthermore, macrophage depletion or SRC inhibition using a novel specific inhibitor significantly inhibited resistant growth. Together, our findings elucidated a novel mechanism of macrophage-induced anti-androgen resistance of metastatic PC and a promising therapeutic approach to treat this deadly disease.

Reconstruction of the gastric cancer microenvironment after neoadjuvant chemotherapy by longitudinal single-cell sequencing.

BACKGROUND: Little is known on the tumor microenvironment (TME) response after neoadjuvant chemotherapy (NACT) in gastric cancer on the molecular level. METHODS: Here, we profiled 33,589 cell transcriptomes in 14 samples from 11 gastric cancer patients (4 pre-treatment samples, 4 post-treatment samples and 3 pre-post pairs) using single-cell RNA sequencing (scRNA-seq) to generate the cell atlas. The ligand-receptor-based intercellular communication networks of the single cells were also characterized before and after NACT. RESULTS: Compered to pre-treatment samples, CD4+ T cells (P = 0.018) and CD8+ T cells (P = 0.010) of post-treatment samples were significantly decreased, while endothelial cells and fibroblasts were increased (P = 0.034 and P = 0.005, respectively). No significant difference observed with respect to CD4+ Tregs cells, cycling T cells, B cells, plasma cells, macrophages, monocytes, dendritic cells, and mast cells (P > 0.05). In the unsupervised nonnegative matrix factorization (NMF) analysis, we revealed that there were three transcriptional programs (NMF1, NMF2 and NMF3) shared among these samples. Compared to pre-treatment samples, signature score of NMF1 was significantly downregulated after treatment (P = 0.009), while the NMF2 signature was significantly upregulated after treatment (P = 0.013). The downregulated NMF1 and upregulated NMF2 signatures were both associated with improved overall survival outcomes based on The Cancer Genome Atlas (TCGA) database. Additionally, proangiogenic pathways were activated in tumor and endothelial cells after treatment, indicating that NACT triggers vascular remodeling by cancer cells together with stromal cells. CONCLUSIONS: In conclusion, our study provided transcriptional profiles of TME between pre-treatment and post-treatment for in-depth understanding on the mechanisms of NACT in gastric cancer and empowering the development of potential optimized therapy procedures and novel drugs.

Interferon-dependent SLC14A1+ cancer-associated fibroblasts promote cancer stemness via WNT5A in bladder cancer.

Cancer-associated fibroblasts (CAFs) play a role in response to cancer treatment and patient prognosis. CAFs show phenotypic and functional heterogeneity and differ widely in tumors of different tissue origin. Here, we use single-cell RNA sequencing of bladder cancer (BC) patient samples and report a CAF subpopulation characterized by overexpression of the urea transporter SLC14A1. This population is induced by interferon signaling and confers stemness to BC cells via the WNT5A paracrine pathway. Activation of cGAS-STING signaling in tumor cells drives interferon production, thereby revealing a link between cGAS-STING signaling and SLC14A1+ CAF differentiation. Furthermore, the inhibition of SLC14A1+ CAF formation via targeting of STAT1 or STING sensitizes tumor cells to chemotherapy. More important, BC patients with high proportions of intratumoral SLC14A1+ CAFs show cancer stage-independent poor outcome and a worse response rate to neoadjuvant chemotherapy or immunotherapy.

Spatially resolved gene regulatory and disease-related vulnerability map of the adult Macaque cortex.

Single cell approaches have increased our knowledge about the cell type composition of the non-human primate (NHP), but a detailed characterization of area-specific regulatory features remains outstanding. We generated single-cell transcriptomic and chromatin accessibility (single-cell ATAC) data of 358,237 cells from prefrontal cortex (PFC), primary motor cortex (M1) and primary visual cortex (V1) of adult female cynomolgus monkey brain, and integrated this dataset with Stereo-seq (spatial enhanced resolution omics-sequencing) of the corresponding cortical areas to assign topographic information to molecular states. We identified area-specific chromatin accessible sites and their targeted genes, including the cell type-specific transcriptional regulatory network associated with excitatory neurons heterogeneity. We reveal calcium ion transport and axon guidance genes related to specialized functions of PFC and M1, identified the similarities and differences between adult macaque and human oligodendrocyte trajectories, and mapped the genetic variants and gene perturbations of human diseases to NHP cortical cells. This resource establishes a transcriptomic and chromatin accessibility combinatory regulatory landscape at a single-cell and spatially resolved resolution in NHP cortex.

Identification of a 3-Gene Model as Prognostic Biomarker in Patients With Gastric Cancer.

Objective: Although the incidence of gastric cancer (GC) is decreasing, GC remains one of the leading cancers in the world. Surgical resection, radiotherapy, chemotherapy, and neoadjuvant therapy have advanced, but patients still face the risk of recurrence and poor prognosis. This study provides new insights for assessment of prognosis and postoperative recurrence of GC patients. Methods: We collected paired cancer and adjacent tissues of 17 patients with early primary GC for bulk transcriptome sequencing. By comparing the transcriptome information of cancer and adjacent cancer, 321 differentially expressed genes (DEGs) were identified. These DEGs were further screened and analyzed with the GC cohort of TCGA to establish a 3-gene prognostic model (PLCL1, PLOD2 and ABCA6). At the same time, the predictive ability of this risk model is validated in multiple public data sets. Besides, the differences in immune cells proportion between the high- and low-risk groups were analyzed by the CIBERSORT algorithm with the Leukocyte signature matrix (LM22) gene signature to reveal the role of the immune microenvironment in the occurrence and development of GC. Results: The model could divide GC samples from TCGA cohorts into two groups with significant differences in overall and disease-free survival. The excellent predictive ability of this model was also validated in multiple other public data sets. The proportion of these immune cells such as resting mast cells, T cells CD4+ memory activated and Macrophages M2 are significantly different between high and low risk group. Conclusion: These three genes used to build the models were validated as biomarkers for predicting tumor recurrence and survival. They may have potential significance for the treatment and diagnosis of patients in the future, and may also promote the development of targeted drugs.

Chord: an ensemble machine learning algorithm to identify doublets in single-cell RNA sequencing data.

High-throughput single-cell RNA sequencing (scRNA-seq) is a popular method, but it is accompanied by doublet rate problems that disturb the downstream analysis. Several computational approaches have been developed to detect doublets. However, most of these methods may yield satisfactory performance in some datasets but lack stability in others; thus, it is difficult to regard a single method as the gold standard which can be applied to all types of scenarios. It is a difficult and time-consuming task for researchers to choose the most appropriate software. We here propose Chord which implements a machine learning algorithm that integrates multiple doublet detection methods to address these issues. Chord had higher accuracy and stability than the individual approaches on different datasets containing real and synthetic data. Moreover, Chord was designed with a modular architecture port, which has high flexibility and adaptability to the incorporation of any new tools. Chord is a general solution to the doublet detection problem.

A single-cell atlas of diffuse large B cell lymphoma.

Diffuse large B cell lymphoma (DLBCL) is one of the most common yet aggressive types of B cell lymphoma and remains incurable in 40% of patients. Herein, we profile the transcriptomes of 94,324 cells from 17 DLBCLs and 3 control samples using single-cell RNA sequencing. Altogether, 73 gene expression programs are identified in malignant cells, demonstrating high intra- and intertumor heterogeneity. Furthermore, 2,754 pairs of suggestive cell-cell interactions are predicted, indicating a complex and highly dynamic tumor microenvironment. Especially for B cell lymphomas, a strong costimulatory CD70-CD27 interaction is predicted between malignant and T cells. Furthermore, coinhibitory signals mediated by TIM3 or TIGIT seem to be the main driving force for T cell exhaustion. Finally, we find that chronic hepatitis B virus infection may have a significant impact on tumor cell survival and immune evasion in DLBCL. Our results provide insights into B cell lymphomagenesis and may facilitate the design of targeted immunotherapy strategies.

Genomic alterations and evolution of cell clusters in metastatic invasive micropapillary carcinoma of the breast.

Invasive micropapillary carcinoma (IMPC) has very high rates of lymphovascular invasion and lymph node metastasis and has been reported in several organs. However, the genomic mechanisms underlying its metastasis are unclear. Here, we perform whole-genome sequencing of tumor cell clusters from primary IMPC and paired axillary lymph node metastases. Cell clusters in multiple lymph node foci arise from a single subclone of the primary tumor. We find evidence that the monoclonal metastatic ancestor in primary IMPC shares high frequency copy-number loss of PRDM16 and IGSF9 and the copy number gain of ALDH2. Immunohistochemistry analysis further shows that low expression of IGSF9 and PRDM16 and high expression of ALDH2 are associated with lymph node metastasis and poor survival of patients with IMPC. We expect these genomic and evolutionary profiles to contribute to the accurate diagnosis of IMPC.

Genome profiles of pathologist-defined cell clusters by multiregional LCM and G&T-seq in one triple-negative breast cancer patient.

Pathological examination is the gold standard for cancer diagnosis, and breast tumor cells are often found in clusters. We report a case study on one triple-negative breast cancer (TNBC) patient, analyzing tumor development, metastasis, and prognosis with simultaneous DNA and RNA sequencing of pathologist-defined cell clusters from multiregional frozen sections. The cell clusters are isolated by laser capture microdissection (LCM) from primary tumor tissue, lymphatic vessels, and axillary lymph nodes. Data are reported for a total of 97 cell clusters. A combination of tumor cell-cluster clonality and phylogeny reveals 3 evolutionarily distinct pathways for this patient, each associated with a unique mRNA signature, and each correlated with disparate survival outcomes. Hub gene analysis indicates that extensive downregulation of ribosomal protein mRNA is a potential marker of poor prognosis in breast cancer.

The Dynamic Changes of Transcription Factors During the Development Processes of Human Biparental and Uniparental Embryos.

Previous studies have revealed that transcription factors (TFs) play important roles in biparental (BI) early human embryogenesis. However, the contribution of TFs during early uniparental embryo development is still largely unknown. Here we systematically studied the expression profiles of transcription factors in early embryonic development and revealed the dynamic changes of TFs in human biparental and uniparental embryogenesis by single-cell RNA sequencing (scRNA-seq). In general, the TF expression model of uniparental embryos showed a high degree of conformity with biparental embryos. The detailed network analysis of three different types of embryos identified that 10 out of 17 hub TFs were shared or specifically owned, such as ZNF480, ZNF581, PHB, and POU5F1, were four shared TFs, ZFN534, GTF3A, ZNF771, TEAD4, and LIN28A, were androgenic (AG) specific TFs, and ZFP42 was the only one parthenogenetic (PG) specific TF. All the four shared TFs were validated using human embryonic stem cell (hESC) differentiation experiments; most of their target genes are responsible for stem cell maintenance and differentiation. We also found that Zf-C2H2, HMG, and MYB were three dominant transcription factor families that appeared in early embryogenesis. Altogether, our work provides a comprehensive regulatory framework and better understanding of TF function in human biparental and uniparental embryogenesis.

Single-cell exome sequencing reveals multiple subclones in metastatic colorectal carcinoma.

BACKGROUND: Colorectal cancer (CRC) is a major cancer type whose mechanism of metastasis remains elusive. METHODS: In this study, we characterised the evolutionary pattern of metastatic CRC (mCRC) by analysing bulk and single-cell exome sequencing data of primary and metastatic tumours from 7 CRC patients with liver metastases. Here, 7 CRC patients were analysed by bulk whole-exome sequencing (WES); 4 of these were also analysed using single-cell sequencing. RESULTS: Despite low genomic divergence between paired primary and metastatic cancers in the bulk data, single-cell WES (scWES) data revealed rare mutations and defined two separate cell populations, indicative of the diverse evolutionary trajectories between primary and metastatic tumour cells. We further identified 24 metastatic cell-specific-mutated genes and validated their functions in cell migration capacity. CONCLUSIONS: In summary, scWES revealed rare mutations that failed to be detected by bulk WES. These rare mutations better define the distinct genomic profiles of primary and metastatic tumour cell clones.

Dissecting spatial heterogeneity and the immune-evasion mechanism of CTCs by single-cell RNA-seq in hepatocellular carcinoma.

Little is known about the transcriptomic plasticity and adaptive mechanisms of circulating tumor cells (CTCs) during hematogeneous dissemination. Here we interrogate the transcriptome of 113 single CTCs from 4 different vascular sites, including hepatic vein (HV), peripheral artery (PA), peripheral vein (PV) and portal vein (PoV) using single-cell full-length RNA sequencing in hepatocellular carcinoma (HCC) patients. We reveal that the transcriptional dynamics of CTCs were associated with stress response, cell cycle and immune-evasion signaling during hematogeneous transportation. Besides, we identify chemokine CCL5 as an important mediator for CTC immune evasion. Mechanistically, overexpression of CCL5 in CTCs is transcriptionally regulated by p38-MAX signaling, which recruites regulatory T cells (Tregs) to facilitate immune escape and metastatic seeding of CTCs. Collectively, our results reveal a previously unappreciated spatial heterogeneity and an immune-escape mechanism of CTC, which may aid in designing new anti-metastasis therapeutic strategies in HCC.

Tumor heterogeneity assessed by sequencing and fluorescence in situ hybridization (FISH) data.

MOTIVATION: Computational reconstruction of clonal evolution in cancers has become a crucial tool for understanding how tumors initiate and progress and how this process varies across patients. The field still struggles, however, with special challenges of applying phylogenetic methods to cancers, such as the prevalence and importance of copy number alteration (CNA) and structural variation events in tumor evolution, which are difficult to profile accurately by prevailing sequencing methods in such a way that subsequent reconstruction by phylogenetic inference algorithms is accurate. RESULTS: In this work, we develop computational methods to combine sequencing with multiplex interphase fluorescence in situ hybridization to exploit the complementary advantages of each technology in inferring accurate models of clonal CNA evolution accounting for both focal changes and aneuploidy at whole-genome scales. By integrating such information in an integer linear programming framework, we demonstrate on simulated data that incorporation of FISH data substantially improves accurate inference of focal CNA and ploidy changes in clonal evolution from deconvolving bulk sequence data. Analysis of real glioblastoma data for which FISH, bulk sequence and single cell sequence are all available confirms the power of FISH to enhance accurate reconstruction of clonal copy number evolution in conjunction with bulk and optionally single-cell sequence data. AVAILABILITY AND IMPLEMENTATION: Source code is available on Github at https://github.com/CMUSchwartzLab/FISH_deconvolution. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Single-cell transcriptomics reveals regulators underlying immune cell diversity and immune subtypes associated with prognosis in nasopharyngeal carcinoma.

Nasopharyngeal carcinoma (NPC) is an aggressive malignancy with extremely skewed ethnic and geographic distributions. Increasing evidence indicates that targeting the tumor microenvironment (TME) represents a promising therapeutic approach in NPC, highlighting an urgent need to deepen the understanding of the complex NPC TME. Here, we generated single-cell transcriptome profiles for 7581 malignant cells and 40,285 immune cells from fifteen primary NPC tumors and one normal sample. We revealed malignant signatures capturing intratumoral transcriptional heterogeneity and predicting aggressiveness of malignant cells. Diverse immune cell subtypes were identified, including novel subtypes such as CLEC9A+ dendritic cells (DCs). We further revealed transcriptional regulators underlying immune cell diversity, and cell-cell interaction analyses highlighted promising immunotherapeutic targets in NPC. Moreover, we established the immune subtype-specific signatures, and demonstrated that the signatures of macrophages, plasmacytoid dendritic cells (pDCs), CLEC9A+ DCs, natural killer (NK) cells, and plasma cells were significantly associated with improved survival outcomes in NPC. Taken together, our findings represent a unique resource providing in-depth insights into the cellular heterogeneity of NPC TME and highlight potential biomarkers for anticancer treatment and risk stratification, laying a new foundation for precision therapies in NPC.

Tumor Copy Number Deconvolution Integrating Bulk and Single-Cell Sequencing Data.

Characterizing intratumor heterogeneity (ITH) is crucial to understanding cancer development, but it is hampered by limits of available data sources. Bulk DNA sequencing is the most common technology to assess ITH, but involves the analysis of a mixture of many genetically distinct cells in each sample, which must then be computationally deconvolved. Single-cell sequencing is a promising alternative, but its limitations-for example, high noise, difficulty scaling to large populations, technical artifacts, and large data sets-have so far made it impractical for studying cohorts of sufficient size to identify statistically robust features of tumor evolution. We have developed strategies for deconvolution and tumor phylogenetics combining limited amounts of bulk and single-cell data to gain some advantages of single-cell resolution with much lower cost, with specific focus on deconvolving genomic copy number data. We developed a mixed membership model for clonal deconvolution via non-negative matrix factorization balancing deconvolution quality with similarity to single-cell samples via an associated efficient coordinate descent algorithm. We then improve on that algorithm by integrating deconvolution with clonal phylogeny inference, using a mixed integer linear programming model to incorporate a minimum evolution phylogenetic tree cost in the problem objective. We demonstrate the effectiveness of these methods on semisimulated data of known ground truth, showing improved deconvolution accuracy relative to bulk data alone.