Single-cell Transcriptomic Datasets Research Articles

Common mitochondrial deletions in RNA-Seq: evaluation of bulk, single-cell, and spatial transcriptomic datasets

Common mitochondrial DNA (mtDNA) deletions are large structural variants in the mitochondrial genome that accumulate in metabolically active tissues with age and have been investigated in various diseases. We applied the Splice-Break2 pipeline (designed for high-throughput quantification of mtDNA deletions) to human RNA-Seq datasets and describe the methodological considerations for evaluating common deletions in bulk, single-cell, and spatial transcriptomics datasets. A robust evaluation of 1570 samples from 14 RNA-Seq studies showed: (i) the abundance of some common deletions detected in PCR-amplified mtDNA correlates with levels observed in RNA-Seq data; (ii) RNA-Seq library preparation method has a strong effect on deletion detection; (iii) deletions had a significant, positive correlation with age in brain and muscle; (iv) deletions were enriched in cortical grey matter, specifically in layers 3 and 5; and (v) brain regions with dopaminergic neurons (i.e., substantia nigra, ventral tegmental area, and caudate nucleus) had remarkable enrichment of common mtDNA deletions.

Technical optimization of spatially resolved single-cell transcriptomic datasets to study clinical liver disease

Single cell and spatially resolved ‘omic’ techniques have enabled deep characterization of clinical pathologies that remain poorly understood, providing unprecedented insights into molecular mechanisms of disease. However, transcriptomic platforms are costly, limiting sample size, which increases the possibility of pre-analytical variables such as tissue processing and storage procedures impacting RNA quality and downstream analyses. Furthermore, spatial transcriptomics have not yet reached single cell resolution, leading to the development of multiple deconvolution methods to predict individual cell types within each transcriptome ‘spot’ on tissue sections. In this study, we performed spatial transcriptomics and single nucleus RNA sequencing (snRNAseq) on matched specimens from patients with either histologically normal or advanced fibrosis to establish important aspects of tissue handling, data processing, and downstream analyses of biobanked liver samples. We observed that tissue preservation technique impacts transcriptomic data, especially in fibrotic liver. Single cell mapping of the spatial transcriptome using paired snRNAseq data generated a spatially resolved, single cell dataset with 24 unique liver cell phenotypes. We determined that cell–cell interactions predicted using ligand–receptor analysis of snRNAseq data poorly correlated with cellular relationships identified using spatial transcriptomics. Our study provides a framework for generating spatially resolved, single cell datasets to study gene expression and cell–cell interactions in biobanked clinical samples with advanced liver disease.

Abstract IA024: Unraveling potential genetic drivers of intratumor transcriptional heterogeneity and phenotypic plasticity in malignant cells

Abstract The concepts of transcriptional intratumor heterogeneity and phenotypic plasticity have gained significant attention in relation to the development of tumor resistance to cancer treatments [1]. In several cancer types, recent studies have linked certain somatic genomic aberrations to the presence of specific transcriptional states in malignant cells [2,3]. However, our general understanding of drivers of epigenetic and transcriptional plasticity stays somewhat fragmented. In theory, this gap could be filled through the generation and analysis of single-cell multi-omics datasets of human tumors coupled with clinical information. In practice, such datasets are pretty scarce, and existing data certainly do not allow for a global analysis of the genetic drivers of cell plasticity and heterogeneity across cancer types. On the other hand, the scientific community has already generated rich bulk transcriptomics datasets coupled with genetic and clinical information (e.g., TCGA) that could be used to answer the question about the relationship between genetic and epigenetic landscapes if proportions of cells in different malignant states could be extracted via a computational deconvolution of the transcriptomic signal. Unfortunately, available methods for accurate bulk data deconvolution require reference single-cell transcriptomics datasets, which limits the applicability of such approaches to study large pan-cancer datasets. We provide a solution to this problem by creating and benchmarking an algorithmic approach that allows extracting the transcriptional heterogeneity information from bulk tumor profiles without the need for a single-cell reference when a matched DNA-sequencing experiment is available. The latter is true for thousands of tumor samples, including all TCGA datasets. We present results of a pan-cancer analysis linking genetic aberrations with proportions of malignant cells in specific transcriptional states that shed light on the potential genetic drivers underlying malignant cell heterogeneity.

Integrating single-cell and bulk transcriptomic analyses to develop a cancer-associated fibroblast-derived biomarker for predicting prognosis and therapeutic response in breast cancer.

Cancer-associated fibroblasts (CAFs) contribute to the progression and treatment of breast cancer (BRCA); however, risk signatures and molecular targets based on CAFs are limited. This study aims to identify novel CAF-related biomarkers to develop a risk signature for predicting the prognosis and therapeutic response of patients with BRCA. CAF-related genes (CAFRGs) and a risk signature based on these genes were comprehensively analyzed using publicly available bulk and single-cell transcriptomic datasets. Modular genes identified from bulk sequencing data were intersected with CAF marker genes identified from single-cell analysis to obtain reliable CAFRGs. Signature CAFRGs were screened via Cox regression and least absolute shrinkage and selection operator (LASSO) analyses. Multiple patient cohorts were used to validate the prognosis and therapeutic responsiveness of high-risk patients stratified based on the CAFRG-based signature. In addition, the relationship between the CAFRG-based signature and clinicopathological factors, tumor immune landscape, functional pathways, chemotherapy sensitivity and immunotherapy sensitivity was examined. External datasets were used and sample experiments were performed to examine the expression pattern of MFAP4, a key CAFRG, in BRCA. Integrated analyses of single-cell and bulk transcriptomic data as well as prognostic screening revealed a total of 43 prognostic CAFRGs; of which, 14 genes (TLN2, SGCE, SDC1, SAV1, RUNX1, PDLIM4, OSMR, NT5E, MFAP4, IGFBP6, CTSO, COL12A1, CCDC8 and C1S) were identified as signature CAFRGs. The CAFRG-based risk signature exhibited favorable efficiency and accuracy in predicting survival outcomes and clinicopathological progression in multiple BRCA cohorts. Functional enrichment analysis suggested the involvement of the immune system, and the immune infiltration landscape significantly differed between the risk groups. Patients with high CAF-related risk scores (CAFRSs) exhibited tumor immunosuppression, enhanced cancer hallmarks and hyposensitivity to chemotherapy and immunotherapy. Five compounds were identified as promising therapeutic agents for high-CAFRS BRCA. External datasets and sample experiments validated the downregulation of MFAP4 and its strong correlation with CAFs in BRCA. A novel CAF-derived gene signature with favorable predictive performance was developed in this study. This signature may be used to assess prognosis and guide individualized treatment for patients with BRCA.

A precise balance of TETRASPANIN1/TORNADO2 activity is required for vascular proliferation and ground tissue patterning in Arabidopsis.

The molecular mechanisms guiding oriented cell divisions in the root vascular tissues of Arabidopsis thaliana are still poorly characterised. By overlapping bulk and single-cell transcriptomic datasets, we unveiled TETRASPANIN1 (TET1) as a putative regulator in this process. TET1 is expressed in root vascular cells, and loss-of-function mutants contain fewer vascular cell files. We further generated and characterised a CRISPR deletion mutant and showed, unlike previously described mutants, that the full knock out is additionally missing endodermal cells in a stochastic way. Finally, we show that HA-tagged versions of TET1 are functional in contrast to fluorescent TET1 translational fusions. Immunostaining using HA-TET1 lines complementing the mutant phenotype suggested a dual plasma membrane and intracellular localisation in the root vasculature and a polar membrane localisation in the young cortex, endodermal and initial cells. Taken together, we show that TET1 is involved in both vascular proliferation and ground tissue patterning. Our initial results pave the way for future work to decipher its precise mode of action.

STEP: profiling cellular-specific targets and pathways of bioactive small molecules in tissues via integrating single-cell transcriptomics and chemoproteomics.

Identifying the cellular targets of bioactive small molecules within tissues has been a major concern in drug discovery and chemical biology research. Compared to cell line models, tissues consist of multiple cell types and complicated microenvironments. Therefore, elucidating the distribution and heterogeneity of targets across various cells in tissues would enhance the mechanistic understanding of drug or toxin action in real-life scenarios. Here, we present a novel multi-omics integration pipeline called Single-cell TargEt Profiling (STEP) that enables the global profiling of protein targets in mammalian tissues with single-cell resolution. This pipeline integrates single-cell transcriptome datasets with tissue-level protein target profiling using chemoproteomics. Taking well-established classic drugs such as aspirin, aristolochic acid, and cisplatin as examples, we confirmed the specificity and precision of cellular drug-target profiles and their associated molecular pathways in tissues using the STEP analysis. Our findings provide more informative insights into the action modes of bioactive molecules compared to in vitro models. Collectively, STEP represents a novel strategy for profiling cellular-specific targets and functional processes with unprecedented resolution.

Comprehensive Integration of Multiple Single-Cell Transcriptomic Data Sets Defines Distinct Cell Populations and Their Phenotypic Changes in Murine Atherosclerosis.

The application of single-cell transcriptomic (single-cell RNA sequencing) analysis to the study of atherosclerosis has provided unique insights into the molecular and genetic mechanisms that mediate disease risk and pathophysiology. However, nonstandardized methodologies and relatively high costs associated with the technique have limited the size and replication of existing data sets and created disparate or contradictory findings that have fostered misunderstanding and controversy. To address these uncertainties, we have performed a conservative integration of multiple published single-cell RNA sequencing data sets into a single meta-analysis, performed extended analysis of native resident vascular cells, and used in situ hybridization to map the disease anatomic location of the identified cluster cells. To investigate the transdifferentiation of smooth muscle cells to macrophage phenotype, we have developed a classifying algorithm based on the quantification of reporter transgene expression. The reporter gene expression tool indicates that within the experimental limits of the examined studies, transdifferentiation of smooth muscle cell to the macrophage lineage is extremely rare. Validated transition smooth muscle cell phenotypes were defined by clustering, and the location of these cells was mapped to lesion anatomy with in situ hybridization. We have also characterized 5 endothelial cell phenotypes and linked these cellular species to different vascular structures and functions. Finally, we have identified a transcriptomically unique cellular phenotype that constitutes the aortic valve. Taken together, these analyses resolve a number of outstanding issues related to differing results reported with vascular disease single-cell RNA sequencing studies, and significantly extend our understanding of the role of resident vascular cells in anatomy and disease.

Integration of single-cell proteomic datasets through distinctive proteins in cell clusters.

The use of mass spectrometry and antibody-based sequencing technologies at the single-cell level has led to an increase in single-cell proteomic datasets. Integrating these datasets is crucial to eliminate the batch effect that often arises due to their limited sequencing molecules. Although methods for horizontally integrating high-dimensional single-cell transcriptomic datasets can also be applied to single-cell proteomic datasets, a specialized approach explicitly tailored for low-dimensional proteomic datasets may enhance the integration process. Here, we introduce SCPRO-HI, an algorithm for the horizontal integration of antibody-based single-cell proteomic datasets. It utilizes a hierarchical cell anchoring technique to match cells based on the similarity of distinctive proteins for constituting cell clusters. A novel variational auto-encoder model is employed for correcting batch effects on the protein abundances, eliminating the need for mapping them into a new domain. Moreover, we propose a technique for extending the algorithm to high-dimensional datasets. The performance of the SCPRO-HI algorithm is evaluated using simulated and real-world single-cell proteomic datasets. The findings demonstrate our algorithm outperforms state-of-the-art methods, achieving a 75% higher silhouette score while preserving HVPs 13% better. Furthermore, the algorithm shows competitive performance in transcriptomic datasets, suggesting potential for integrating high-dimensional mass-spectrometry-based proteomic datasets.

Transcriptomic and behavioral analyses reveal unique target tissues and molecular pathways associated with embryonic exposure to low level glyphosate and metal mixtures in zebrafish

Investigation of developmental molecular events following exposure to environmentally relevant agrochemical mixtures is critical to predicting their potential long-term ecological and human health risks. Here, we sought to uncover transcriptomic changes during zebrafish (Danio rerio) embryonic development following exposure to glyphosate and co-exposure to metals. Glyphosate is widely used globally with an allowable drinking water limit of 700 ppb. We examined effects of glyphosate (10 ppb) alone and when co-exposed to a metal mixture containing low levels of arsenic (4 ppb), lead (5 ppb), cadmium (2 ppb), and vanadium (15 ppb). This mixture was derived based on behavioral and morphological toxicity findings and environmentally relevant concentrations found in agricultural regions where glyphosate and metals are ubiquitously present. Gene expression patterns coupled to a single-cell transcriptomic dataset revealed that developmental exposure (28–72 h post fertilization) to glyphosate dysregulates expression of developmental genes specific to the central nervous system. Subsequent studies indicated significant suppression of larval zebrafish movement with 10 ppb glyphosate exposure. Studies with glyphosate + metals mixture and metals mixture alone showed unique developmental transcriptomic patterns and behavioral changes compared to glyphosate exposure alone. However, some outcomes (e.g., changes in expression of genes involved in epigenetic regulation and extracellular matrix patterning) were common across all three exposures compared to the control. Notably, glyphosate + metals co-exposure distinctly suppresses lysosomal transcripts and targets renal developmental genes. While further studies are required to uncover the precise nature of the interactions between glyphosate and metals, our study shows that glyphosate at very low levels is a behavioral and neurotoxicant that changes when metals are present. Given this herbicide affects distinctive physiological processes, including renal development and lysosomal dysregulation when co-exposed with metals, we conclude that environmental cation levels should be considered in glyphosate toxicity and risk assessment.

Analysis of multiple programmed cell death-related prognostic genes and functional validations of necroptosis-associated genes in oesophageal squamous cell carcinoma

Oesophageal squamous cell carcinoma (ESCC) is a lethal malignancy. Immune checkpoint inhibitors (ICIs) showed great clinical benefits for patients with ESCC. We aimed to construct a model predicting prognosis and response to ICIs by integrating diverse programmed cell death (PCD) forms. Genes related to 14 PCDs were collected to generate multi-gene signatures, including apoptosis, necroptosis, pyroptosis, ferroptosis, and cuproptosis. Bulk and single-cell RNA transcriptome datasets were used to develop and validate the model. We assessed the functions of two necroptosis-related genes in ESCC cells by Western blot, co-immunoprecipitation (Co-IP), LDH release assay, CCK-8, and migration assay, followed by immunohistochemistry (IHC) staining on samples of patients with ESCC (n=67). We built and validated a 16-gene prognostic combined cell death index (CCDI) by combining immunogenic cell death (ICD) and necroptosis signatures. The CCDI could also predict response to ICIs in cancer, as shown by Tumour Immune Dysfunction and Exclusion (TIDE) analysis, confirmed in four independent ICI clinical trials. Trajectory analysis revealed that HOOK1 and CUL4A might affect ESCC cell fate. We found that HOOK1 induced necroptosis and inhibited the proliferation and migration of ESCC cells, while CUL4A exhibited the opposite effects. Co-IP assay confirmed that HOOK1 and CUL4A promoted and reduced necrosome formation in ESCC cells. Data from patients with ESCC further supported that HOOK1 and CUL4A might be a tumour suppressor and oncogene, respectively. We constructed a CCDI model with potential in predicting prognosis and response to ICIs in cancer. HOOK1 and CUL4A in the CCDI model are crucial prognostic biomarkers in ESCC. The Natural Science Foundation of China [82172786], The National Cancer Center Climbing Fund of China [NCC201908B06], The Natural Science Foundation of Heilongjiang Province [LH2021H077].

SPP1+ TAM subpopulations in tumor microenvironment promote intravasation and metastasis of head and neck squamous cell carcinoma.

Macrophages are heterogeneous cells that play multifaceted roles in cancer progression and metastasis. However, the phenotypic diversity of tumor-associated macrophages (TAMs) in head and neck squamous carcinomas (HNSCC) remains poorly characterized. Here, we comprehensively analyzed the HNSCC single-cell transcriptomic dataset (GSE172577) and identified 5 subsets of myeloid-driven cells as TAMs using Seurat. Deciphering the lineage trajectory of TAMs, we revealed that FCN1+ TAMs could give rise to pro-angiogenesis SPP1+CCL18+ and SPP1+FOLR2+ populations through SPP1-CCL18+ and CXCL9+CXCL10+ TAMs. SPP1+CCL18+ and SPP1+FOLR2+ TAMs harbored pro-angiogenic and metastatic transcriptional programs and were correlated with poor survival of HNSCC patients. Our immunostaining examination revealed that infiltration of SPP1+ TAMs is associated with lymph node metastasis and poor prognosis in patients with HNSCC. Cell-cell communication analysis implied that SPP1+ TAM populations may employ SPP1 signaling to activate metastasis-related ECs. In vitro and in vivo studies, we demonstrated that SPP1hi TAMs enhanced tumor intravasation and metastasis in HNSCC in a manner dependent on the secretion of SPP1, CCL18, and CXCL8. Taken together, our study characterized the cellular heterogeneity of TAM populations and identified two SPP1+ TAM populations that play key roles in HNSCC intravasation and metastasis and serve as predictive markers for patients with HNSCC.

Identification of cytokine-induced cell communications by pan-cancer meta-analysis.

Cancer immune responses are complex cellular processes in which cytokine-receptor interactions play central roles in cancer development and response to therapy; dysregulated cytokine-receptor communication may lead to pathological processes, including cancer, autoimmune diseases, and cytokine storm; however, our knowledge regarding cytokine-mediated cell-cell communication (CCI) in different cancers remains limited. The present study presents a single-cell and pan-cancer-level transcriptomics integration of 41,900 cells across 25 cancer types. We developed a single-cell method to actively express 62 cytokine-receptor pairs to reveal stable cytokine-mediated cell communications involving 84 cytokines and receptors. The correlation between the sample-based CCI profile and the interactome analysis indicates multiple cytokine-receptor modules including TGFB1, IL16ST, IL15, and the PDGF family. Some isolated cytokine interactions, such as FN1-IL17RC, displayed diverse functions within over ten single-cell transcriptomics datasets. Further functional enrichment analysis revealed that the constructed cytokine-receptor interaction map is associated with the positive regulation of multiple immune response pathways. Using public TCGA pan-cancer mutational data, co-mutational analysis of the cytokines and receptors provided significant co-occurrence features, implying the existence of cooperative mechanisms. Analysis of 10,967 samples from 32 TCGA cancer types revealed that the 84 cytokine and receptor genes are significantly associated with clinical survival time. Interestingly, the tumor samples with mutations in any of the 84 cytokines and receptors have a substantially higher mutational burden, offering insights into antitumor immune regulation and response. Clinical cancer stage information revealed that tumor samples with mutations in any of the 84 cytokines and receptors stratify into earlier tumor stages, with unique cellular compositions and clinical outcomes. This study provides a comprehensive cytokine-receptor atlas of the cellular architecture in multiple cancers at the single-cell level.

FastCAR: fast correction for ambient RNA to facilitate differential gene expression analysis in single-cell RNA-sequencing datasets

Cell type-specific differential gene expression analyses based on single-cell transcriptome datasets are sensitive to the presence of cell-free mRNA in the droplets containing single cells. This so-called ambient RNA contamination may differ between samples obtained from patients and healthy controls. Current ambient RNA correction methods were not developed specifically for single-cell differential gene expression (sc-DGE) analyses and might therefore not sufficiently correct for ambient RNA-derived signals. Here, we show that ambient RNA levels are highly sample-specific. We found that without ambient RNA correction, sc-DGE analyses erroneously identify transcripts originating from ambient RNA as cell type-specific disease-associated genes. We therefore developed a computationally lean and intuitive correction method, Fast Correction for Ambient RNA (FastCAR), optimized for sc-DGE analysis of scRNA-Seq datasets generated by droplet-based methods including the 10XGenomics Chromium platform. FastCAR uses the profile of transcripts observed in libraries that likely represent empty droplets to determine the level of ambient RNA in each individual sample, and then corrects for these ambient RNA gene expression values. FastCAR can be applied as part of the data pre-processing and QC in sc-DGE workflows comparing scRNA-Seq data in a health versus disease experimental design. We compared FastCAR with two methods previously developed to remove ambient RNA, SoupX and CellBender. All three methods identified additional genes in sc-DGE analyses that were not identified in the absence of ambient RNA correction. However, we show that FastCAR performs better at correcting gene expression values attributed to ambient RNA, resulting in a lower frequency of false-positive observations. Moreover, the use of FastCAR in a sc-DGE workflow increases the cell-type specificity of sc-DGE analyses across disease conditions.

Cross-species single-cell landscape of vertebrate pineal gland.

The pineal gland has evolved from a photoreceptive organ in fish to a neuroendocrine organ in mammals. This study integrated multiple daytime single-cell RNA-seq datasets from the pineal glands of zebrafish, rats, and monkeys, providing a detailed examination of the evolutionary transition at single-cell resolution. We identified key factors responsible for the anatomical and functional transformation of the pineal gland. We retrieved and integrated daytime single-cell transcriptomic datasets from the pineal glands of zebrafish, rats, and monkeys, resulting in a total of 22 431 cells after rigorous quality filtering. Comparative analysis was then conducted to elucidate the evolution of pineal cells, their photosensitivity, their role in melatonin production, and the signaling processes within the glands of these species. Our analysis identified distinct cellular compositions of the pineal gland in zebrafish, rats, and monkeys. Zebrafish photoreceptors exhibited comprehensive phototransduction gene expression, while specific genes, including transducin (Gngt1, Gnb3, and Gngt2) and phosducin (Pdc), were consistently present in mammalian pinealocytes. We found transcriptional similarities between the pineal gland and retina, underscoring shared evolutionary and functional pathways. Zebrafish displayed unique light-responsive circadian gene activity compared to rats and monkeys. Key ligand-receptor interactions were identified, especially involving MDK and PTN, influencing melatonin synthesis across species. Furthermore, we observed species-specific GPCR (G protein-coupled receptors) expressions related to melatonin synthesis and their alignment with retinal expressions. Our findings also highlighted specific transcription factors (TFs) and regulatory networks associated with pineal gland evolution and function. Our study provides a detailed analysis of the pineal gland's evolution from fish to mammals. We identified key transcriptional changes and controls that highlight the gland's functional diversity. Notably, we found significant ligand-receptor interactions influencing melatonin synthesis and demonstrated parallels between pineal and retinal expressions. These insights enhance our understanding of the pineal gland's role in phototransduction, melatonin production, and circadian rhythms in vertebrates.

Tumor endothelial cell autophagy is a key vascular-immune checkpoint in melanoma.

Tumor endothelial cells (TECs) actively repress inflammatory responses and maintain an immune-excluded tumor phenotype. However, the molecular mechanisms that sustain TEC-mediated immunosuppression remain largely elusive. Here, we show that autophagy ablation in TECs boosts antitumor immunity by supporting infiltration and effector function of T-cells, thereby restricting melanoma growth. In melanoma-bearing mice, loss of TEC autophagy leads to the transcriptional expression of an immunostimulatory/inflammatory TEC phenotype driven by heightened NF-kB and STING signaling. In line, single-cell transcriptomic datasets from melanoma patients disclose an enriched InflammatoryHigh /AutophagyLow TEC phenotype in correlation with clinical responses to immunotherapy, and responders exhibit an increased presence of inflamed vessels interfacing with infiltrating CD8+ T-cells. Mechanistically, STING-dependent immunity in TECs is not critical for the immunomodulatory effects of autophagy ablation, since NF-kB-driven inflammation remains functional in STING/ATG5 double knockout TECs. Hence, our study identifies autophagy as a principal tumor vascular anti-inflammatory mechanism dampening melanoma antitumor immunity.

Adipose cDC1s contribute to obesity-associated inflammation through STING-dependent IL-12 production.

Obesity is associated with chronic low-grade white adipose tissue (WAT) inflammation that can contribute to the development of insulin resistance in mammals. Previous studies have identified interleukin (IL)-12 as a critical upstream regulator of WAT inflammation and metabolic dysfunction during obesity. However, the cell types and mechanisms that initiate WAT IL-12 production remain unclear. Here we show that conventional type 1 dendritic cells (cDC1s) are the cellular source of WAT IL-12 during obesity through analysis of mouse and human WAT single-cell transcriptomic datasets, IL-12 reporter mice and IL-12p70 protein levels by enzyme-linked immunosorbent assay. We demonstrate that cDC1s contribute to obesity-associated inflammation by increasing group 1 innate lymphocyte interferon-γ production and inflammatory macrophage accumulation. Inducible depletion of cDC1s increased WAT insulin sensitivity and systemic glucose tolerance during diet-induced obesity. Mechanistically, endocytosis of apoptotic bodies containing self-DNA by WAT cDC1s drives stimulator of interferon genes (STING)-dependent IL-12 production. Together, these results suggest that WAT cDC1s act as critical regulators of adipose tissue inflammation and metabolic dysfunction during obesity.

A rapid and sensitive, multiplex, whole mount RNA fluorescence in situ hybridization and immunohistochemistry protocol

BackgroundIn the past few years, there has been an explosion in single-cell transcriptomics datasets, yet in vivo confirmation of these datasets is hampered in plants due to lack of robust validation methods. Likewise, modeling of plant development is hampered by paucity of spatial gene expression data. RNA fluorescence in situ hybridization (FISH) enables investigation of gene expression in the context of tissue type. Despite development of FISH methods for plants, easy and reliable whole mount FISH protocols have not yet been reported.ResultsWe adapt a 3-day whole mount RNA-FISH method for plant species based on a combination of prior protocols that employs hybridization chain reaction (HCR), which amplifies the probe signal in an antibody-free manner. Our whole mount HCR RNA-FISH method shows expected spatial signals with low background for gene transcripts with known spatial expression patterns in Arabidopsis inflorescences and monocot roots. It allows simultaneous detection of three transcripts in 3D. We also show that HCR RNA-FISH can be combined with endogenous fluorescent protein detection and with our improved immunohistochemistry (IHC) protocol.ConclusionsThe whole mount HCR RNA-FISH and IHC methods allow easy investigation of 3D spatial gene expression patterns in entire plant tissues.

Single-cell dissection of the multicellular ecosystem and molecular features underlying microvascular invasion in HCC.

Microvascular invasion (MVI) is a crucial pathological hallmark of HCC that is closely associated with poor outcomes, early recurrence, and intrahepatic metastasis following surgical resection and transplantation. However, the intricate tumor microenvironment and transcriptional programs underlying MVI in HCC remain poorly understood. We performed single-cell RNA sequencing of 46,789 individual cells from 10 samples of MVI+ (MVI present) and MVI- (MVI absent) patients with HCC. We conducted comprehensive and comparative analyses to characterize cellular and molecular features associated with MVI and validated key findings using external bulk, single-cell, and spatial transcriptomic datasets coupled with multiplex immunofluorescence assays. The comparison identified specific subtypes of immune and stromal cells critical to the formation of the immunosuppressive and pro-metastatic microenvironment in MVI+ tumors, including cycling T cells, lysosomal associated membrane protein 3+ dendritic cells, triggering receptor expressed on myeloid cells 2+ macrophages, myofibroblasts, and arterial i endothelial cells. MVI+ malignant cells are characterized by high proliferation rates, whereas MVI- malignant cells exhibit an inflammatory milieu. Additionally, we identified the midkine-dominated interaction between triggering receptor expressed on myeloid cells 2+ macrophages and malignant cells as a contributor to MVI formation and tumor progression. Notably, we unveiled a spatially co-located multicellular community exerting a dominant role in shaping the immunosuppressive microenvironment of MVI and correlating with unfavorable prognosis. This study provides a comprehensive single-cell atlas of MVI in HCC, shedding light on the complex multicellular ecosystem and molecular features associated with MVI. These findings deepen our understanding of the underlying mechanisms driving MVI and provide valuable insights for improving clinical diagnosis and developing more effective treatment strategies.

Apolipoprotein L genes are novel mediators of inflammation in beta cells

Aims/hypothesisInflammation induces beta cell dysfunction and demise but underlying molecular mechanisms remain unclear. The apolipoprotein L (APOL) family of genes has been associated with innate immunity and apoptosis in non-pancreatic cell types, but also with metabolic syndrome and type 2 diabetes mellitus. Here, we hypothesised that APOL genes play a role in inflammation-induced beta cell damage.MethodsWe used single-cell transcriptomics datasets of primary human pancreatic islet cells to study the expression of APOL genes upon specific stress conditions. Validation of the findings was carried out in EndoC-βH1 cells and primary human islets. Finally, we performed loss- and gain-of-function experiments to investigate the role of APOL genes in beta cells.ResultsAPOL genes are expressed in primary human beta cells and APOL1, 2 and 6 are strongly upregulated upon inflammation via the Janus kinase (JAK)−signal transducer and activator of transcription (STAT) pathway. APOL1 overexpression increases endoplasmic reticulum stress while APOL1 knockdown prevents cytokine-induced beta cell death and interferon-associated response. Furthermore, we found that APOL genes are upregulated in beta cells from donors with type 2 diabetes compared with donors without diabetes mellitus.Conclusions/interpretationAPOLs are novel regulators of islet inflammation and may contribute to beta cell damage during the development of diabetes.Data availabilityscRNAseq data generated by our laboratory and used in this study are available in the Gene Expression Omnibus (GEO; www.ncbi.nlm.nih.gov/geo/), accession number GSE218316.Graphical

Bone Marrow Microenvironment and Hematopoietic Cell Types Are Differentially Affected By Transcriptomic Quality Control Filtering Metrics in Single Cell Data

A crucial step prior to analysis of single cell transcriptomic data includes filtering out low-quality (e.g., dying) cells and artifacts (e.g., empty droplets). Choosing appropriate quality control parameters leads to correct cell assignment, so that the data for downstream analysis is high-quality and is representative of all processed cell types. These metrics include high and low cut-offs for mitochondrial content, total number of genes, number of different genes, and ribosomal content per cell. They have been found to vary based on cell and tissue type, as well biological variables such as age and disease, and technical variables such as sequencing technology. The aim of this study was to investigate the variation of these quality control metrics in a data set that included stem, progenitor, and differentiated cell types of the hematopoietic, mesenchymal, and endothelial lineages. Furthermore, we set out to determine whether applying standard cut-offs may differentially affect different cell types. We calculated individual filtering parameters for each cell type using the R package ddqc (Subramanian et al. Genome Biol. 2022). Thus, any biological variability based on mitochondrial, ribosomal, and gene counts was accounted for. Notably, distribution of measurements for each of the above quality control metrics varied by cluster, and certain cell-type means were adjacent to commonly used cut-off values. For instance, we observed higher content of mitochondrial genes in osteoblasts, sinusoidal endothelial cells, a subset of adipocyte-primed mesenchymal cells (MSPC), as well as hematopoietic progenitor cells, pre-B, and B cells. On the other hand, subsets of erythroblasts, neutrophils, as well as arteriolar endothelial cells had the lowest mitochondrial content. In addition, lower total gene counts and lower gene complexity were found in adipocyte-primed MSPCs, neutrophils, and megakaryocyte-erythrocyte progenitors, while higher counts were found in granulocyte-monocyte progenitor cells, megakaryocyte/erythroid-primed multipotent progenitor cells and subsets of erythroblasts. Applying universal cutoff values to this data set would greatly reduce, and some cases, eliminate cell types that have high mitochondrial content or lower gene counts and complexity, while leaving those cell types on the opposite spectrum unaffected. This would also skew proportions of cells in downstream analyses, thus affecting biological interpretation of the results. Overall, our results demonstrate biological variability of the quality control metric distributions in a single cell transcriptomic data set that includes diverse bone marrow niche and hematopoietic cell types. This reveals novel biological information regarding distinct cell types as well as cautions that applying standard filtering paraments on such a data set would eliminate some cell types and bias downstream analysis for others. Optimization of filtering criteria will be important when broadening single cell analysis to multiplexed approaches incorporating transcriptome, genome, epigenome, and proteome-level data.