Single-cell Expression Profiling Research Articles

Single Cell RNA Sequencing of Sequential Samples before and after BCMA-Directed CAR-T Reveal Features Associated with Non-Durable Response, Exhausted T-Cells and Decreased Expression of Genes Encoding Key Surface Targets in Particular in Patients with Extramedullary Disease

Introduction Single cell sequencing confers a unique opportunity to study the complex interplay between the malignant plasma cells (PCs) and the host tumor microenvironment (TME) within the bone marrow niche. Pre-treatment factors associated with the achievement of durable responses from anti-B-cell maturation antigen (BCMA) directed CART cell therapy remain poorly understood. Herein, we performed a longitudinal single cell analysis to identify factors associated with response, or therapeutic failure, to FDA approved BCMA-directed CART cell therapy. Methods We performed single-cell RNA sequencing (scRNA-seq) to comprehensively characterize both the cellular and molecular properties of malignant plasma cells and TME. Separated CD138+ and CD138- components were universally encapsulated within hours of pre-CART baseline (BL) and day 90 post treatment with commercial CAR-T using 10x Genomics. Libraries were sequenced separately on an Illumina NovaSeq 6000 instrument. Cell Ranger pipelines with default parameters were used to trim reads, align, count unique molecular identifiers, and call cells. Downstream processing was carried out using Seurat. Batch effects were corrected using canonical correlation analysis (CCA) integration. After quality control for feature counts and mitochondrial gene expression, there were a total of 110,923 single cells, including 48,150 plasma cells (PC) and 62,773 TME cells. In addition, we integrated these data with an internal repository of bulk RNA sequencing (RNAseq) (N=844 CD138+ MM cells) to evaluate co-regulation of genes (Renatino-Canevarolo et al, ASH 2022). Patients were split into two broad groups, durable (DR) vs non-durable response (NDR) based on PFS cutoff of 9 months. Given its negative impact on outcomes, we also examined differences between those with or without extra-medullary disease (EMD). Results Median PFS for the total cohort of N=13 at the time of data cutoff was 332 days (range 279-569) for DR and 28 days (12-197) for NDR. Malignant cells were identified based on inferred copy number alterations and comparison to benign plasma cell controls from sequenced healthy donors. Single-cell expression profiles of plasma cells are primarily clustered by patient, whereas expression of non-plasma cells largely clustered by cell types. In DR, expression of BL malignant PCs revealed enriched expression of genes encoding several known surface markers, including FCRL5, CD38, TACI and BCMA, and genes associated with a more plasma-cell-like phenotype, such as XBP1, whereas EEF2 and CD48/SLAMF2 were increased in BL PCs from NDR.Pathway analysis of DR vs NDR revealed upregulated activity genes involved in the interleukin-6 pathway, interferon γ and oxidative phosphorylation (padj <0.1). In contrast, MYC pathways and unfolded protein response genes (padj < 0.1) were significantly upregulated in NDR ( Figure 1). Analysis of the BL TME illustrated fewer CD4 naïve, CD4 effector memory (EM), T effector memory RA-positive (TEMRA) cells and higher CD8 central memory (CM) cells in NDR vs DR patients. Exhaustion signatures were higher among BL CD4 and CD8 cells pre-CART in NDR relative to DR, and in D90 samples this was also more pronounced among CD4 cells in NDR vs DR samples. Comparing D90 to BL, DR patients had fewer Tregs, increased numbers of trafficking monocytes and more proliferating CD8 T-cells. Finally, looking at patients with EMD, differences in surface target gene expression were noted with decreased expression of BCMA, TACI, FCRL5, and CD38 observed, although CD44 and Ly9 expression appeared equivalent or enriched in those with known EMD (Figure 2). Interrogation of bulk RNAseq revealed that several of these genes, including BCMA, PRDM1/Blimp-1, XBP1, CD138 are co-regulated in a common geneset. This geneset was significantly downregulated in patients with NDR vs DR. Conclusion These data highlight the pre-treatment features of both tumor and T-cell that can pre-dispose real-world patients with RRMM to early relapse after anti-BCMA CAR-T, notably pre-existing T-cell exhaustion, and the presence of highly proliferative plasma cells with underlying resistance mechanisms, including downregulation of several known immunotherapy targets, especially in those with co-existing EMD. Ciara L Freeman, Xiaohong Zhao & Mark B Meads contributed equally

MACC: a visual interactive knowledgebase of metabolite-associated cell communications.

Metabolite-associated cell communications play critical roles in maintaining the normal biological function of human through coordinating cells, organsand physiological systems. Though substantial information of MACCs has been continuously reported, no relevant database has become available so far. To address this gap, we here developed the first knowledgebase (MACC), to comprehensively describe human metabolite-associated cell communications through curation of experimental literatures. MACC currently contains: (a) 4206 carefully curated metabolite-associated cell communications pairs involving 244 human endogenous metabolites and reported biological effects in vivo and in vitro; (b) 226 comprehensive cell subtypes and 296 disease states, such as cancers, autoimmune diseases, and pathogenic infections; (c) 4508 metabolite-related enzymes and transporters, involving 542 pathways; (d) an interactive tool with user-friendly interface to visualize networks of multiple metabolite-cell interactions. (e) overall expression landscape of metabolite-associated gene sets derived from over 1500 single-cell expression profiles to infer metabolites variations across different cells in the sample. Also, MACC enables cross-links to well-known databases, such as HMDB, DrugBank, TTD and PubMed etc. In complement to ligand-receptor databases, MACC may give new perspectives of alternative communication between cells via metabolite secretion and adsorption, together with the resulting biological functions. MACC is publicly accessible at: http://macc.badd-cao.net/.

Cell Type Catalog of Middle Turbinate Epithelium (Cell Catalog of Turbinate Epithelium)

AbstractObjectiveSingle‐cell RNA‐sequencing of middle turbinate mucosa was performed to create the first single‐cell transcriptome catalog of this part of the human body.Study DesignBasic science research.SettingSingle center, tertiary care center.MethodsSamples were obtained from the head of the middle turbinate from a healthy volunteer. After the specimen was prepared per lab protocol, cells were dissociated, resuspended, and counted. Single‐cell libraries were then prepared according to the 10x Genomics protocol and sequenced using NovaSeq 6000 (Illumina). Sequencing data were processed using Cell Ranger, and clustering and gene expression analysis was performed using Seurat. Cell types were annotated through expression profiling of single cells using known markers and data from other single‐cell studies.ResultsFourteen unique cell types were identified, including serous, goblet, club, basal, ciliated, endothelial, and mesenchymal cells, as well as multiple types of blood cells.ConclusionThis catalog provides a comprehensive depiction of the cellular composition of middle turbinate mucosa. By uncovering the cellular stratification of gene expression profiles in the healthy middle turbinate epithelium, the groundwork has been laid for further investigation into the molecular pathogenesis and targeted therapy of sinonasal disease.

Spatial atlas of the mouse central nervous system at molecular resolution

Spatially charting molecular cell types at single-cell resolution across the 3D volume is critical for illustrating the molecular basis of brain anatomy and functions. Single-cell RNA sequencing has profiled molecular cell types in the mouse brain1,2, but cannot capture their spatial organization. Here we used an in situ sequencing method, STARmap PLUS3,4, to profile 1,022 genes in 3D at a voxel size of 194 × 194 × 345 nm3, mapping 1.09 million high-quality cells across the adult mouse brain and spinal cord. We developed computational pipelines to segment, cluster and annotate 230 molecular cell types by single-cell gene expression and 106 molecular tissue regions by spatial niche gene expression. Joint analysis of molecular cell types and molecular tissue regions enabled a systematic molecular spatial cell-type nomenclature and identification of tissue architectures that were undefined in established brain anatomy. To create a transcriptome-wide spatial atlas, we integrated STARmap PLUS measurements with a published single-cell RNA-sequencing atlas1, imputing single-cell expression profiles of 11,844 genes. Finally, we delineated viral tropisms of a brain-wide transgene delivery tool, AAV-PHP.eB5,6. Together, this annotated dataset provides a single-cell resource that integrates the molecular spatial atlas, brain anatomy and the accessibility to genetic manipulation of the mammalian central nervous system.

Single-Cell RNA Sequencing of Donor-Reactive T Cells Reveals Role of Apoptosis in Donor-Specific Hyporesponsiveness of Kidney Transplant Recipients.

After kidney transplantation (KT), donor-specific hyporesponsiveness (DSH) of recipient T cells develops over time. Recently, apoptosis was identified as a possible underlying mechanism. In this study, both transcriptomic profiles and complete V(D)J variable regions of TR transcripts from individual alloreactive T cells of kidney transplant recipients were determined with single-cell RNA sequencing. Alloreactive T cells were identified by CD137 expression after stimulation of peripheral blood mononuclear cells obtained from KT recipients (N = 7) prior to and 3-5 years after transplantation with cells of their donor or a third party control. The alloreactive T cells were sorted, sequenced and the transcriptome and T cell receptor profiles were analyzed using unsupervised clustering. Alloreactive T cells retain a highly polyclonal T Cell Receptor Alpha/Beta repertoire over time. Post transplantation, donor-reactive CD4+ T cells had a specific downregulation of genes involved in T cell cytokine-mediated pathways and apoptosis. The CD8+ donor-reactive T cell profile did not change significantly over time. Single-cell expression profiling shows that activated and pro-apoptotic donor-reactive CD4+ T cell clones are preferentially lost after transplantation in stable kidney transplant recipients.

Single-cell transcriptome analysis revealing the intratumoral heterogeneity of ccRCC and validation of MT2A in pathogenesis.

Clear-cell renal cell carcinoma (ccRCC) appears as the most common type of kidney cancer, the carcinogenesis of which has not been fully elucidated. Tumor heterogeneity plays a crucial role in cancer progression, which could be largely deciphered by the implement of scRNA-seq. The bulk and single-cell RNA expression profile is obtained from TCGA and study conducted by Young et al. We utilized UMAP, TSNE, and clustering algorithm Louvain for dimensionality reduction and FindAllMarkers function for determining the DEGs. Monocle2 was utilized to perform pseudo-time series analysis. SCENIC was implemented for transcription factor analysis of each cell subgroup. A series of WB, CFA, CCK-8, and EDU analysis was utilized for the validation of the role of MT2A in ccRCC carcinogenesis. We observed higher infiltration of T/NK and B cells in tumorous tissues, indicating the role of immune cells in ccRCC carcinogenesis. Transcription factor analysis revealed the activation of EOMES and ETS1 in CD8 + T cells, while CAFs were divided into myo-CAFs and i-CAFs, with i-CAFs showing distinct enrichment of ATF3, JUND, JUNB, EGR1, and XBP1. Through cell trajectory analysis, we discerned three distinct stages of cellular evolution, where State2 symbolizes normal renal tubular cells that underwent transitions into State1 and State3 as the CNV score ascended. Functional enrichment examination revealed an amplification of interferon gamma and inflammatory response pathways within tumor cells. The consensus clustering algorithm yielded two molecular subtypes, with cluster 2 being associated with advanced tumor stages and an abundance of infiltrated immune cells. We identified 17 prognostic genes through Cox and LASSO regression models and used them to construct a prognostic model, the efficacy of which was verified in multiple cohorts. Furthermore, we investigated the role of MT2A, one of our hub genes, in ccRCC carcinogenesis, and found it to regulate proliferation and migration of malignant cells. We depicted a detailed single-cell landscape of ccRCC, with special focus on CAFs, endothelial cells, and renal tubular cells. A prognostic model of high stability and accuracy was constructed based on the DEGs. MT2A was found to be actively implicated in ccRCC carcinogenesis, regulating proliferation and migration of the malignant cells.

Abstract PR005: An atlas of cellular heterogeneity in primary and metastatic renal cell carcinomas

Abstract Renal cell carcinoma (RCC) tumors harbor various layers of intra-tumor heterogeneity at the genetic, transcriptomic, and cellular levels. A series of recent studies have underlined the importance of charting this cellular variability to understand the cell of origin of various RCCs and the mechanisms that underlie their response to therapy. However, the regulatory factors driving intra-tumor heterogeneity in RCCs, the role of this heterogeneity in driving metastasis, and its association with clinical outcomes are mostly unknown. To address these questions, we have generated a cellular map of transcriptional heterogeneity in RCCs, which consists of >100,000 single-cell expression profiles from six patient-derived xenografts (PDX) models, including models derived from primary and brain metastatic tumors, as well as ten primary and six metastatic patient tumor tissues. We also developed a computational method for Gene Expression Decomposition and Integration (GEDI) that enables seamless integration of single-cell transcriptomic data across heterogeneous cancer samples while providing interpretable axes of variation. Using GEDI, we performed a fine-grained analysis of the heterogeneity of cancer cells and found that, despite patient-specific differences, common sources of intra-tumor heterogeneity exist across samples, driven by variable activity of pathways such as TNF-α/NF-κB signaling and oxidative phosphorylation (OxPhos). Analysis of data from our PDX models confirmed that this heterogeneity was stable and reproducible. A major source of intra-tumor heterogeneity was hypoxia signaling, even in VHL-deficient clear cell RCC samples (ccRCC), challenging the traditional view that ccRCC cells have a uniform pseudo-hypoxic status due to VHL inactivation. Regulatory network activity projection allowed us to disentangle the contributions of Hypoxia Inducible Factors HIF1A and HIF2A, revealing their divergent regulatory programs: while HIF1A is associated with cell-cycle and proliferation signatures, the activity of HIF2A correlates with epithelial-mesenchymal transition (EMT) within tumors, which we validated by RNA sequencing of HIF1A and EPAS1 knockdown in VHL-negative cells. Next, we used GEDI to study the cell state changes that occur during metastasis in both neoplastic and non-neoplastic tumor-infiltrating cells. By examining the differences in gene expression patterns between primary and metastatic samples, we identified various pathways that are transcriptionally activated during this transition, including EMT and Oxphos. We also uncovered distinct subpopulations of cancer cells with high gene expression changes associated with a metastatic profile. Analysis of the ligand-receptor interactions in the tumor microenvironment revealed that interactions from malignant cells to immune cells were over-represented in metastatic tumors. Overall, our cellular atlas has uncovered a complex continuum of cell states in RCCs, highlighting various drivers of the intra-tumor heterogeneity and establishing various cell states associated with metastasis. Citation Format: Ariel Madrigal, Minjun Kim, Adrien Osakwe, Tianyuan Lu, Zohreh Mehrjoo, Elham Moslemi, Rick Farouni, Larisa Morales-Soto, Yu Chang Wang, Matthew Dankner, Haig Djambazian, Kevin Petrecca, Jonathan Spicer, Fadi Brimo, Peter Siegel, Morag Park, Jiannis Ragoussis, Simon Tanguay, Yasser Riazalhosseini, Hamed S. Najafabadi. An atlas of cellular heterogeneity in primary and metastatic renal cell carcinomas [abstract]. In: Proceedings of the AACR Special Conference: Advances in Kidney Cancer Research; 2023 Jun 24-27; Austin, Texas. Philadelphia (PA): AACR; Cancer Res 2023;83(16 Suppl):Abstract nr PR005.

Ontogenetically distinct neutrophils differ in function and transcriptional profile in zebrafish

The current view of hematopoiesis considers leukocytes on a continuum with distinct developmental origins, and which exert non-overlapping functions. However, there is less known about the function and phenotype of ontogenetically distinct neutrophil populations. In this work, using a photoconvertible transgenic zebrafish line; Tg(mpx:Dendra2), we selectively label rostral blood island-derived and caudal hematopoietic tissue-derived neutrophils in vivo during steady state or upon injury. By comparing the migratory properties and single-cell expression profiles of both neutrophil populations at steady state we show that rostral neutrophils show higher csf3b expression and migration capacity than caudal neutrophils. Upon injury, both populations share a core transcriptional profile as well as subset-specific transcriptional signatures. Accordingly, both rostral and caudal neutrophils are recruited to the wound independently of their distance to the injury. While rostral neutrophils respond uniformly, caudal neutrophils respond heterogeneously. Collectively, our results reveal that co-existing neutrophils populations with ontogenically distinct origin display functional differences.

Immune landscape in rejection of renal transplantation revealed by high-throughput single-cell RNA sequencing.

Background: The role of the cellular level in kidney transplant rejection is unclear, and single-cell RNA sequencing (scRNA-seq) can reveal the single-cell landscape behind rejection of human kidney allografts at the single-cell level. Methods: High-quality transcriptomes were generated from scRNA-seq data from five human kidney transplantation biopsy cores. Cluster analysis was performed on the scRNA-seq data by known cell marker genes in order to identify different cell types. In addition, pathways, pseudotime developmental trajectories and transcriptional regulatory networks involved in different cell subpopulations were explored. Next, we systematically analyzed the scoring of gene sets regarding single-cell expression profiles based on biological processes associated with oxidative stress. Results: We obtained 81,139 single cells by scRNA-seq from kidney transplant tissue biopsies of three antibody-mediated rejection (ABMR) patients and two acute kidney injury (AKI) patients with non-rejection causes and identified 11 cell types, including immune cells, renal cells and several stromal cells. Immune cells such as macrophages showed inflammatory activation and antigen presentation and complement signaling, especially in rejection where some subpopulations of cells specifically expressed in rejection showed specific pro-inflammatory responses. In addition, patients with rejection are characterized by an increased number of fibroblasts, and further analysis of subpopulations of fibroblasts revealed their involvement in inflammatory and fibrosis-related pathways leading to increased renal rejection and fibrosis. Notably, the gene set score for response to oxidative stress was higher in patients with rejection. Conclusion: Insight into histological differences in kidney transplant patients with or without rejection was gained by assessing differences in cellular levels at single-cell resolution. In conclusion, we applied scRNA-seq to rejection after renal transplantation to deconstruct its heterogeneity and identify new targets for personalized therapeutic approaches.

Single-cell protein expression profiling resolves circulating and resident memory T cell diversity across tissues and infection contexts

Memory CD8+ Tcells can be broadly divided into circulating (TCIRCM) and tissue-resident memory T (TRM) populations. Despite well-defined migratory and transcriptional differences, the phenotypic and functional delineation of TCIRCM and TRM cells, particularly across tissues, remains elusive. Here, we utilized an antibody screening platform and machine learning prediction pipeline (InfinityFlow) to profile >200 proteins in TCIRCM and TRM cells in solid organs and barrier locations. High-dimensional analyses revealed unappreciated heterogeneity within TCIRCM and TRM cell lineages across nine different organs after either local or systemic murine infection models. Additionally, we demonstrated the relative effectiveness of strategies allowing for the selective ablation of TCIRCM or TRM populations across organs and identified CD55, KLRG1, CXCR6, and CD38 as stable markers for characterizing memory Tcell function during inflammation. Together, these data and analytical framework provide an in-depth resource for memory Tcell classification in both steady-state and inflammatory conditions.

Single-cell expression profile of Drosophila ovarian follicle stem cells illuminates spatial differentiation in the germarium

BackgroundHow stem cell populations are organized and regulated within adult tissues is important for understanding cancer origins and for developing cell replacement strategies. Paradigms such as mammalian gut stem cells and Drosophila ovarian follicle stem cells (FSC) are characterized by population asymmetry, in which stem cell division and differentiation are separately regulated processes. These stem cells behave stochastically regarding their contributions to derivative cells and also exhibit dynamic spatial heterogeneity. Drosophila FSCs provide an excellent model for understanding how a community of active stem cells maintained by population asymmetry is regulated. Here, we use single-cell RNA sequencing to profile the gene expression patterns of FSCs and their immediate derivatives to investigate heterogeneity within the stem cell population and changes associated with differentiation.ResultsWe describe single-cell RNA sequencing studies of a pre-sorted population of cells that include FSCs and the neighboring cell types, escort cells (ECs) and follicle cells (FCs), which they support. Cell-type assignment relies on anterior–posterior (AP) location within the germarium. We clarify the previously determined location of FSCs and use spatially targeted lineage studies as further confirmation. The scRNA profiles among four clusters are consistent with an AP progression from anterior ECs through posterior ECs and then FSCs, to early FCs. The relative proportion of EC and FSC clusters are in good agreement with the prevalence of those cell types in a germarium. Several genes with graded profiles from ECs to FCs are highlighted as candidate effectors of the inverse gradients of the two principal signaling pathways, Wnt and JAK-STAT, that guide FSC differentiation and division.ConclusionsOur data establishes an important resource of scRNA-seq profiles for FSCs and their immediate derivatives that is based on precise spatial location and functionally established stem cell identity, and facilitates future genetic investigation of regulatory interactions guiding FSC behavior.

Single-cell mRNA expression profiling of chemosensitive locus coeruleus neurons from American bullfrogs

The locus coeruleus (LC) is a cluster of neurons in the midbrain that, among other roles, participates in the control of breathing by sensing CO2 and evoking hyperventilation, which is observed in mammals, amphibians, and reptiles. The LC is considered the major noradrenergic nucleus of the brain, but in addition to norepinephrine, the mammalian LC also produces glutamate and GABA. Yet, little is known about the neurotransmitter phenotypes of chemosensitive LC neurons in other vertebrates. Thus, we investigated the neurotransmitter profile of neurons responsible for CO2/pH chemoreception in American bullfrogs. For that, 19 LC neurons from 5 American bullfrogs ( Lithobates catesbeianus) brains were analyzed. Chemosensitivity was ensured by monitoring the increase of firing in response to hypercapnia (switching from 1.5% to 5% CO2) in whole-cell patch clamp. A hypercapnic response was observed in all neurons recorded, which were then aspirated into the recording pipette and preserved. Single-cell quantitative PCR was performed to measure the mRNA copy number of markers for glutamatergic (glutamate transporter, VGLUT2), GABAergic (GABA catalytic enzyme, GAD1), and norepinephrinergic (norepinephrine biosynthetic enzyme, DBH) neurons. In addition, we measured the expression of genes with a potential role in pH/CO2 sensitivity; KCNK5 that encodes TASK-2, an alkaline-activated K+ channel; ASIC2 that encodes an acid-sensing cation channel; and KCNJ16 that encodes Kir 5.1, a K+ channel inhibited by acidosis. All LC neurons expressed both VGLUT2 and DBH, while GAD1 had a minimal expression, supporting the role of cotransmission using glutamate and norepinephrine in chemosensitive neurons. KCNK5 and ASIC2 were expressed in most neurons, while KCNJ16 was not. This result suggests that TASK-2 and ASIC2 proteins may contribute to CO2/pH chemosensitivity of LC neurons. Moreover, the expression of these pH sensing related genes ( KCNK5 and ASIC2) had expression linearly correlated to the noradrenergic marker DBH ( e.g., when DHB was high, KCNK5 was high and vice versa) but were not correlated to VGLUT2. Our results point to a transcriptional program that links the noradrenergic cell identity, but not the glutamatergic identity, with the capacity to sense CO2/pH. Given the comparable role of LC in regulating blood gases across vertebrates, our results suggest that the cotransmission of norepinephrine and glutamate from chemosensitive LC neurons plays a role in CO2/pH sensing. This research was funded by the Department of Defense (W911NF2010275) and NIH (R01NS114514) This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Spatial metabolic heterogeneity of oligodendrogliomas at single-cell resolution.

Oligodendrogliomas are a type of rare and incurable gliomas whose metabolic profiles have yet to be fully examined. The present study examined the spatial differences in metabolic landscapes underlying oligodendrogliomas and should provide unique insights into the metabolic characteristics of these uncommon tumors. Single-cell RNA-sequencing expression profiles from 4044 oligodendroglioma cells derived from tumors resected from four locations frontal, temporal, parietal, and frontotemporoinsular) and in which 1p/19q co-deletion and IDH1 or IDH2 mutations were confirmed were computationally analyzed through a robust workflow to elucidate relative differences in metabolic pathway activities among the different locations. Dimensionality reduction using metabolic expression profiles exhibited clustering corresponding to each location subgroup. From the 80 metabolic pathways examined, over 70 pathways had significantly different activity scores between location subgroups. Further analysis of metabolic heterogeneity suggests that mitochondrial oxidative phosphorylation accounts for considerable metabolic variation within the same locations. Steroid and fatty acid metabolism pathways were also found to be major contributors to heterogeneity. Oligodendrogliomas display distinct spatial metabolic differences in addition to intra-location metabolic heterogeneity.

Prognostic value of autophagy-related genes based on single-cell RNA-sequencing in colorectal cancer.

Background: Colorectal cancer (CRC) is the second most common cancer in China. Autophagy plays an important role in the initiation and development of CRC. Here, we assessed the prognostic value and potential functions of autophagy-related genes (ARGs) using integrated analysis using single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) and RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA). Methods: We analyzed GEO-scRNA-seq data from GEO using various single-cell technologies, including cell clustering, and identification of differentially expressed genes (DEGs) in different cell types. Additionally, we performed gene set variation analysis (GSVA). The differentially expressed ARGs among different cell types and those between CRC and normal tissues were identified using TCGA-RNA-seq data, and the hub ARGs were screened. Finally, a prognostic model based on the hub ARGs was constructed and validated, and patients with CRC in TCGA datasets were divided into high- and low-risk groups based on their risk-score, and immune cells infiltration and drug sensitivity analyses between the two groups were performed. Results: We obtained single-cell expression profiles of 16,270 cells, and clustered them into seven types of cells. GSVA revealed that the DEGs among the seven types of cells were enriched in many signaling pathways associated with cancer development. We screened 55 differentially expressed ARGs, and identified 11 hub ARGs. Our prognostic model revealed that the 11 hub ARGs including CTSB, ITGA6, and S100A8, had a good predictive ability. Moreover, the immune cell infiltrations in CRC tissues were different between the two groups, and the hub ARGs were significantly correlated with the enrichment of immune cell infiltration. The drug sensitivity analysis revealed that the patients in the two risk groups had difference in their response to anti-cancer drugs. Conclusion: We developed a novel prognostic 11-hub ARG risk model, and these hubs may act as potential therapeutic targets for CRC.

Single-Cell Sequencing-Based Validation of T Cell-Associated Diagnostic Model Genes and Drug Response in Crohn's Disease.

Crohn's disease is a highly heterogeneous autoimmune disease with a unique inflammatory phenotype of T cells at the lesion site. We aim to further explore the diagnosis of Crohn's disease and drug prediction of T cell marker gene expression. We obtained single-cell expression profile data from 22 CDs or normal samples and performed cell annotation and cellular communication analysis. Through the intersection of T cell marker genes, differential genes, and WGCNA results, we identified T cell-specific key genes and their immune landscapes and potential pathogenesis, and validated them across multiple datasets and patient tissue samples. We also explored the differentiation characteristics of genes by pseudo-temporal analysis and assessed their diagnostic performance and drug sensitivity by molecular docking. Finally, we extended this study to the prognosis of IBD-associated colon cancer. TNF-centered 5-gene diagnostic model not only has excellent diagnostic efficacy, but is also closely associated with KRAS, P53, and IL6/JAK/STAT3 pathways and physiological processes, such as EMT, coagulation, and apoptosis. In addition, this diagnostic model may have potential synergistic immunotherapeutic effects, with positive correlations with immune checkpoints such as CTLA4, CD86, PDCD1LG2, and CD40. Molecular docking demonstrated that BIRC3 and ANXA1 have strong binding properties to Azathioprine and Glucoocorticoid. Furthermore, the 5-gene model may suggest antagonism to IFX and prognosis for colon cancer associated with inflammatory bowel disease. Single-cell sequencing targeting T cell-related features in patients with Crohn's disease may aid in new diagnostic decisions, as well as the initial exploration of high-potential therapies.

Single-Cell Profiling of Tumor Immune Microenvironment Reveals Immune Irresponsiveness in Gastric Signet-Ring Cell Carcinoma

Gastric cancer (GC) is a major cancer type characterized by high heterogeneity in both tumor cells and the tumor immune microenvironment (TIME). One intractable GC subtype is gastric signet-ring cell carcinoma (GSRCC), which is associated with poor prognosis. However, it remains unclear what the GSRCC TIME characteristics are and how these characteristics may contribute to clinical outcomes. We enrolled 32 patients with advanced GC of diverse subtypes and profiled their TIME using an immune-targeted single-cell profiling strategy, including (i) immune-targeted single-cell RNA sequencing (scRNA-seq, n = 20 patients) and (ii) protein expression profiling by a targeted antibody panel for mass cytometry (CyTOF, n = 12 patients). We also generated matched V(D)J sequencing of T and B cells along CD45+ immunocytes. We find that compared to non-GSRCC, the GSRCC TIME appears to be quiescent where both CD4+ and CD8+ T cells are difficult to be mobilized, which further impairs the proper functions of B cells. CXCL13, mainly produced by Tfh, Th17, and exhausted CD8+ T cells, is a central coordinator of this transformation. We show that CXCL13 expression can predict the response to immune checkpoint blockade in GC patients, which may be related to its effects on tertiary lymphoid structures. Our study provides a comprehensive molecular portrait of immune cell compositions and cell states in advanced GC patients, highlighting adaptive immune irresponsiveness in GSRCC and a mediator role of CXCL13 in TIME. Our targeted single-cell transcriptomic and proteomic profiling represents a powerful approach for TIME-oriented translational research.

Single Cell Metabolic Landscape of Pituitary Neuroendocrine Tumor Subgroups and Lineages.

Pituitary neuroendocrine tumors (PitNETs) are common intracranial tumors comprising numerous subtypes whose metabolic profiles have yet to be fully examined. The present in silico study analyzed single-cell expression profiles from 2311 PitNET cells from various lineages and subtypes to elucidate differences in metabolic activities. Gonadotroph tumors exhibited high activities with histidine metabolism, whose activity is low in lactotroph tumors. Somatotroph tumors enriched for sulfur and tyrosine metabolism, while lactotroph tumors were enriched metabolism of nitrogen, ascorbate, and aldarate. PIT-1 lineage tumors exhibited high sulfur and thiamine metabolism. These results set precedence for further translational studies for subgroup/lineage specific targeted therapies.

Innovations in hematopoietic stem-cell mobilization: a review of the novel CXCR4 inhibitor motixafortide.

Hematopoietic stem-cell transplantation (HCT) and stem-cell-based gene therapies rely on the ability to collect sufficient CD34+ hematopoietic stem and progenitor cells (HSPCs), typically via peripheral blood mobilization. Commonly used HSPC mobilization regimens include single-agent granulocyte colony-stimulating factor (G-CSF), plerixafor, chemotherapy, or a combination of these agents. These regimens, however, frequently require multiple days of injections and leukapheresis procedures to collect adequate HSPCs for HCT (minimum = >2 × 106 CD34+ cells/kg; optimal = 5-6 × 106 CD34+ cells/kg). In addition, these regimens frequently yield suboptimal CD34+ HSPC numbers for HSPC-based gene-edited therapies, given the significantly higher HSPC number needed for successful gene-editing and manufacturing. Meanwhile, G-CSF is associated with common adverse events such as bone pain as well as an increased risk of rare but potentially life-threatening splenic rupture. Moreover, G-CSF is unsafe in patients with sickle-cell disease, a key patient population that may benefit from autologous HSPC-based gene-edited therapies, where it has been associated with unacceptable rates of serious vaso-occlusive and thrombotic events. Motixafortide is a novel CXCR4 inhibitor with extended in vivo activity (>48 h) that has been shown in preclinical and clinical trials to rapidly mobilize robust numbers of HSPCs in preparation for HCT, while preferentially mobilizing increased numbers of more primitive HSPCs by immunophenotyping and single-cell RNA expression profiling. In this review, we present a history of stem-cell mobilization and update of recent innovations in novel mobilization strategies with a specific focus on the development of motixafortide, a long-acting CXCR4 inhibitor, as a novel HSPC mobilizing agent.

Single cell biology to analyse cellular heterogeneity in the response to amyloid plaques in Alzheimer’s Disease

AbstractMicroglial activation and neuroinflammation are crucial in the pathogenesis of Alzheimer’s disease. However, studies in mouse models and human postmortem samples have yielded divergent results regarding transcriptional programs in microglia elicited by exposure to amyloid‐b pathology. Here, we investigate 127,000 single cell expression profiles of human microglia isolated freshly from a xenotransplantation model for AD. Human microglia adopt a different, more complex spectrum of cell states than mouse microglia. Besides a disease associated (DAM) profile, human microglia show a pronounced HLA‐response related to antigen presentation in response to amyloid‐b plaques, which is absent in mouse. Furthermore, oligomeric amyloid‐β present in this model induces a distinctive pro‐inflammatory CRM response characterized by the expression of cytokines and chemokines, also not seen in mice. TREM2 and APOE polymorphisms modulate the response of microglia to amyloid‐b plaques, much less to oligomeric Aβ. In conclusion, human microglia mount a multipronged ARM response to amyloid pathology in Alzheimer’s disease. This implies that therapeutic strategies targeting microglia need to analyse carefully the effect on these different cell states some of which seem protective.

Single-cell and microarray chip analysis revealed the underlying pathogenesis of ulcerative colitis and validated model genes in diagnosis and drug response

The morbidity rate of ulcerative colitis (UC) in the world is increasing year by year, recurrent episodes of diarrhea, mucopurulent and bloody stools, and abdominal pain are the main symptoms, reducing the quality of life of the patient and affecting the productivity of the society. In this study, we sought to develop robust diagnostic biomarkers for UC, to uncover potential targets for anti-TNF-ɑ drugs, and to investigate their associated pathway mechanisms. We collected single-cell expression profile data from 9 UC or healthy samples and performed cell annotation and cell communication analysis. Revealing the possible pathogenesis of ulcerative colitis by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) analysis. Based on the disease-related modules obtained from weighted correlation network analysis (WGCNA) analysis, we used Lasso regression analysis and random forest algorithm to identify the genes with the greatest impact on disease (EPB41L3, HSD17B3, NDRG1, PDIA5, TRPV3) and further validated the diagnostic value of the model genes by various means. To further explore the relationship and mechanism between model genes and drug sensitivity, we collected gene expression profiles of 185 UC patients before receiving anti-tumor necrosis factor drugs, and we performed functional analysis based on the results of differential analysis between NR tissues and R tissues, and used single-sample GSEA (ssGSEA) and CIBERSORT algorithms to explore the important role of immune microenvironment on drug sensitivity. The results suggest that our model is not only helpful in aiding diagnosis, but also has implications for predicting drug efficacy; in addition, model genes may influence drug sensitivity by affecting immune cells. We suggest that this study has developed a diagnostic model with higher specificity and sensitivity, and also provides suggestions for clinical administration and drug efficacy prediction.