Metabolic heterogeneity in clear cell renal cell carcinoma revealed by single-cell RNA sequencing and spatial transcriptomics

BACKGROUND

Clear cell renal cell carcinoma is a prototypical tumor characterized by metabolic reprogramming, which extends beyond tumor cells to encompass diverse cell types within the tumor microenvironment. Nonetheless, current research on metabolic reprogramming in renal cell carcinoma mostly focuses on either tumor cells alone or conducts analyses of all cells within the tumor microenvironment as a mixture, thereby failing to precisely identify metabolic changes in different cell types within the tumor microenvironment.

METHODS

Gathering 9 major single-cell RNA sequencing databases of clear cell renal cell carcinoma, encompassing 195 samples. Spatial transcriptomics data were selected to conduct metabolic activity analysis with spatial localization. Developing scMet program to convert RNA-seq data into scRNA-seq data for downstream analysis.

RESULTS

Diverse cellular entities within the tumor microenvironment exhibit distinct infiltration preferences across varying histological grades and tissue origins. Higher-grade tumors manifest pronounced immunosuppressive traits. The identification of tumor cells in the RNA splicing state reveals an association between the enrichment of this particular cellular population and an unfavorable prognostic outcome. The energy metabolism of CD8+ T cells is pivotal not only for their cytotoxic effector functions but also as a marker of impending cellular exhaustion. Sphingolipid metabolism evinces a correlation with diverse macrophage-specific traits, particularly M2 polarization. The tumor epicenter is characterized by heightened metabolic activity, prominently marked by elevated tricarboxylic acid cycle and glycolysis while the pericapsular milieu showcases a conspicuous enrichment of attributes associated with vasculogenesis, inflammatory responses, and epithelial-mesenchymal transition. The scMet facilitates the transformation of RNA sequencing datasets sourced from TCGA into scRNA sequencing data, maintaining a substantial degree of correlation.

CONCLUSIONS

The tumor microenvironment of clear cell renal cell carcinoma demonstrates significant metabolic heterogeneity across various cell types and spatial dimensions. scMet exhibits a notable capability to transform RNA sequencing data into scRNA sequencing data with a high degree of correlation.

Plant biotechnology research with single-cell transcriptome: recent advancements and prospects

KEY MESSAGE

Single-cell transcriptomic techniques have emerged as powerful tools in plant biology, offering high-resolution insights into gene expression at the individual cell level. This review highlights the rapid expansion of single-cell technologies in plants, their potential in understanding plant development, and their role in advancing plant biotechnology research. Single-cell techniques have emerged as powerful tools to enhance our understanding of biological systems, providing high-resolution transcriptomic analysis at the single-cell level. In plant biology, the adoption of single-cell transcriptomics has seen rapid expansion of available technologies and applications. This review article focuses on the latest advancements in the field of single-cell transcriptomic in plants and discusses the potential role of these approaches in plant development and expediting plant biotechnology research in the near future. Furthermore, inherent challenges and limitations of single-cell technology are critically examined to overcome them and enhance our knowledge and understanding.

A ubiquitin-proteasome system-related signature to predict prognosis, immune infiltration, and therapy efficacy for breast cancer

The ubiquitin-proteasome system (UPS) is an essential regulatory system for maintaining homeostasis, and its dysfunction may cause various diseases. The activity of proteasome and ubiquitin-conjugating enzymes has been found to be greatly increased in breast cancer (BC), indicating that the heterogeneity of UPS may be related to the progression of BC. Gene data was obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases and performed in multiple algorithms to construct a UPS-related signature for BC. Patients in the UPS low-risk group had greater overall and recurrence-free survival probability than those in the UPS high-risk group. This signature was closely associated with functional enrichment. Some high metabolism-related pathways were more active in the UPS high-risk group. The UPS low-risk group had more abundant anti-tumor immune cells, while in the UPS high-risk group, immunosuppressive cells were dominant. More importantly, we found that the UPS low-risk group was more sensitive to immunotherapy, while the UPS high-risk group responded better to radiotherapy. Drug sensitivity analysis identified more effective chemotherapy drugs in different UPS-related risk groups. This UPS-related signature may serve as a novel biomarker and independent prognostic factor for BC. It can effectively predict prognosis, immune infiltration, and therapy efficacy, providing new strategies for individualized treatment.

STGNNks: Identifying cell types in spatial transcriptomics data based on graph neural network, denoising auto-encoder, and k-sums clustering

BACKGROUND

Spatial transcriptomics technologies fully utilize spatial location information, tissue morphological features, and transcriptional profiles. Integrating these data can greatly advance our understanding about cell biology in the morphological background.

METHODS

We developed an innovative spatial clustering method called STGNNks by combining graph neural network, denoising auto-encoder, and k-sums clustering. First, spatial resolved transcriptomics data are preprocessed and a hybrid adjacency matrix is constructed. Next, gene expressions and spatial context are integrated to learn spots' embedding features by a deep graph infomax-based graph convolutional network. Third, the learned features are mapped to a low-dimensional space through a zero-inflated negative binomial (ZINB)-based denoising auto-encoder. Fourth, a k-sums clustering algorithm is developed to identify spatial domains by combining k-means clustering and the ratio-cut clustering algorithms. Finally, it implements spatial trajectory inference, spatially variable gene identification, and differentially expressed gene detection based on the pseudo-space-time method on six 10x Genomics Visium datasets.

RESULTS

We compared our proposed STGNNks method with five other spatial clustering methods, CCST, Seurat, stLearn, Scanpy and SEDR. For the first time, four internal indicators in the area of machine learning, that is, silhouette coefficient, the Davies-Bouldin index, the Caliniski-Harabasz index, and the S_Dbw index, were used to measure the clustering performance of STGNNks with CCST, Seurat, stLearn, Scanpy and SEDR on five spatial transcriptomics datasets without labels (i.e., Adult Mouse Brain (FFPE), Adult Mouse Kidney (FFPE), Human Breast Cancer (Block A Section 2), Human Breast Cancer (FFPE), and Human Lymph Node). And two external indicators including adjusted Rand index (ARI) and normalized mutual information (NMI) were applied to evaluate the performance of the above six methods on Human Breast Cancer (Block A Section 1) with real labels. The comparison experiments elucidated that STGNNks obtained the smallest Davies-Bouldin and S_Dbw values and the largest Silhouette Coefficient, Caliniski-Harabasz, ARI and NMI, significantly outperforming the above five spatial transcriptomics analysis algorithms. Furthermore, we detected the top six spatially variable genes and the top five differentially expressed genes in each cluster on the above five unlabeled datasets. And the pseudo-space-time tree plot with hierarchical layout demonstrated a flow of Human Breast Cancer (Block A Section 1) progress in three clades branching from three invasive ductal carcinoma regions to multiple ductal carcinoma in situ sub-clusters.

CONCLUSION

We anticipate that STGNNks can efficiently improve spatial transcriptomics data analysis and further boost the diagnosis and therapy of related diseases. The codes are publicly available at https://github.com/plhhnu/STGNNks.

Microbiota-mediated shaping of mouse spleen structure and immune function characterized by scRNA-seq and Stereo-seq

Gut microbes exhibit complex interactions with their hosts and shape an organism's immune system throughout its lifespan. As the largest secondary lymphoid organ, the spleen has a wide range of immunological functions. To explore the role of microbiota in regulating and shaping the spleen, we employ scRNA-seq and Stereo-seq technologies based on germ-free (GF) mice to detect differences in tissue size, anatomical structure, cell types, functions, and spatial molecular characteristics. We identify 18 cell types, 9 subtypes of T cells, and 7 subtypes of B cells. Gene differential expression analysis reveals that the absence of microorganisms results in alterations in erythropoiesis within the red pulp region and congenital immune deficiency in the white pulp region. Stereo-seq results demonstrate a clear hierarchy of immune cells in the spleen, including marginal zone (MZ) macrophages, MZ B cells, follicular B cells and T cells, distributed in a well-defined pattern from outside to inside. However, this hierarchical structure is disturbed in GF mice. Ccr7 and Cxcl13 chemokines are specifically expressed in the spatial locations of T cells and B cells, respectively. We speculate that the microbiota may mediate the structural composition or partitioning of spleen immune cells by modulating the expression levels of chemokines.

Liver in infections: a single-cell and spatial transcriptomics perspective

The liver is an immune organ that plays a vital role in the detection, capture, and clearance of pathogens and foreign antigens that invade the human body. During acute and chronic infections, the liver transforms from a tolerant to an active immune state. The defence mechanism of the liver mainly depends on a complicated network of intrahepatic and translocated immune cells and non-immune cells. Therefore, a comprehensive liver cell atlas in both healthy and diseased states is needed for new therapeutic target development and disease intervention improvement. With the development of high-throughput single-cell technology, we can now decipher heterogeneity, differentiation, and intercellular communication at the single-cell level in sophisticated organs and complicated diseases. In this concise review, we aimed to summarise the advancement of emerging high-throughput single-cell technologies and re-define our understanding of liver function towards infections, including hepatitis B virus, hepatitis C virus, Plasmodium, schistosomiasis, endotoxemia, and corona virus disease 2019 (COVID-19). We also unravel previously unknown pathogenic pathways and disease mechanisms for the development of new therapeutic targets. As high-throughput single-cell technologies mature, their integration into spatial transcriptomics, multiomics, and clinical data analysis will aid in patient stratification and in developing effective treatment plans for patients with or without liver injury due to infectious diseases.

Improvements and challenges of tissue preparation for spatial transcriptome analysis of skull base tumors

Background

Spatial transcriptome (ST) provides molecular profiles of tumor cells at the spatial level, which brings new progress to the research of tumors and the tumor microenvironment. This study summarizes the experiences and lessons learned in the spatial section preparation of two different pathological types of nose and skull base tumors at our institution, with the aim of offering guidelines to researchers to avoid wasting precious samples and provide a basis for the application of ST in clinical practice.

Methods

Frozen tissue blocks from patients with squamous cell carcinoma and adenocarcinoma of the nose and skull base diagnosed at our institution were prepared. The effects of different procedures and pathological tissue types on slide quality were explored and evaluated using RNA integrity number (RIN) and HE scores as criteria. The effects of different RIN values on ST sequencing data were explored.

Results

A total of 43 samples were obtained from 26 patients, including 22 with squamous carcinomas and 21 with adenocarcinomas. Thirteen samples with satisfactory RNA quality control and good histological morphology were sequenced for ST. Sample isolation time <15 min and abandonment of snap-frozen isopentane significantly improved RNA quality (p = 0.004, p < 0.0001) and histomorphological integrity (p = 0.02, p = 0.02). Selection of a suitable tissue RNA extraction kit was critical for RNA quality (p < 0.0001). No difference between 6 ≤ RIN <7 and RIN >7 in ST sequencing results was found, indicating that RIN ≥6 can be used as a criterion for qualified RNA quality control. Therefore, fresh tissues washed as soon as possible with cold PBS and then dried using OCT for snap freezing are currently the best method for preparing spatial sections of nose and skull base tumor tissues of different pathological types.

Conclusion

This study is the first to investigate the feasibility of applying ST to different pathological types of nose and skull base tumors and to demonstrate the widespread application of ST in tumors. Rational optimization of spatial slide preparation procedures and exploration of individualized pre-sequencing protocols are used as the first stage to ensure the quality of spatial sequencing and lay the foundation for subsequent spatial analysis.

Identification and Localization of Cell Types in the Mouse Olfactory Bulb Using Slide-SeqV2

Spatial transcriptomics maps RNA molecules to the location in a tissue where they are expressed. Here we document the use of Slide-SeqV2 to visualize gene expression in the mouse olfactory bulb (OB). This approach relies on spatially identified beads to locate and quantify individual transcripts. The expression profiles associated with the beads are used to identify and localize individual cell types in an unbiased manner. We demonstrate the various cell types and subtypes with distinct spatial locations in the olfactory bulb that are identified using Slide-SeqV2.

Deconvolution algorithms for inference of the cell-type composition of the spatial transcriptome

The spatial transcriptome has enabled researchers to resolve transcriptome expression profiles while preserving information about cell location to better understand the complex biological processes that occur in organisms. Due to technical limitations, the current high-throughput spatial transcriptome sequencing methods (known as next-generation sequencing with spatial barcoding methods or spot-based methods) cannot achieve single-cell resolution. A single measurement site, called a spot, in these technologies frequently contains multiple cells of various types. Computational tools for determining the cellular composition of a spot have emerged as a way to break through these limitations. These tools are known as deconvolution tools. Recently, a couple of deconvolution tools based on different strategies have been developed and have shown promise in different aspects. The resulting single-cell resolution expression profiles and/or single-cell composition of spots will significantly affect downstream data mining; thus, it is crucial to choose a suitable deconvolution tool. In this review, we present a list of currently available tools for spatial transcriptome deconvolution, categorize them based on the strategies they employ, and explain their advantages and limitations in detail in order to guide the selection of these tools in future studies.

Identification of two cancer stem cell-like populations in triple-negative breast cancer xenografts

Gene expression analysis at the single-cell level by next-generation sequencing has revealed the existence of clonal dissemination and microheterogeneity in cancer metastasis. The current spatial analysis technologies can elucidate the heterogeneity of cell–cell interactions in situ. To reveal the regional and expressional heterogeneity in primary tumors and metastases, we performed transcriptomic analysis of microtissues dissected from a triple-negative breast cancer (TNBC) cell line MDA-MB-231 xenograft model with our automated tissue microdissection punching technology. This multiple-microtissue transcriptome analysis revealed three cancer cell-type clusters in the primary tumor and axillary lymph node metastasis, two of which were cancer stem cell (CSC)-like clusters (CD44/MYC-high, HMGA1-high). Reanalysis of public single-cell RNA-sequencing datasets confirmed that the two CSC-like populations existed in TNBC xenograft models and in TNBC patients. The diversity of these multiple CSC-like populations could cause differential anticancer drug resistance, increasing the difficulty of curing this cancer.

Summary: We identified two types of cancer stem cell (CSC)-like populations in triple-negative breast cancer xenografts and patients. These CSC-like populations could potentially make tumors more drug resistant and thus more difficult to treat.

Click chemistry-based amplification and detection of endogenous RNA and DNA molecules in situ using clampFISH probes

Direct labeling and measurement of gene expression in single cells show the tremendous variability otherwise hidden in bulk measurements. Single-molecule RNA fluorescence in situ hybridization (FISH) has become a mainstay in laboratories worldwide for measuring gene expression with precision. However, this method remains relatively low throughput because the total fluorescent signal produced is weak and requires long exposure times and high magnification microscopy, which limits the total number of cells sampled in each image. As such, it is experimentally difficult and time-consuming to sample a large enough population of cells to visualize and quantify specific gene expression of rare cells directly. Several FISH-based tools were recently developed that retain single-molecule sensitivity and specificity while greatly amplifying the fluorescent signal, thus making FISH-based analysis possible using standard microscopes with low magnification objectives. These tools have also enabled the detection of smaller and more specific targets like splice junctions or single nucleotide polymorphisms. Here we will describe one such tool, clampFISH, an oligonucleotide-based fluorescence amplification strategy for visualizing genomic loci and individual RNA transcripts in fixed cells. ClampFISH maintains specificity while amplifying fluorescent signals, making it amenable to high throughput assays such as low magnification microscopy, spatial transcriptomics, and flow sorting. The clampFISH technique involves probing the target RNA or DNA using a series of C-shaped oligonucleotide probes, each with a 3' azide and a 5' alkyne. Hybridization of the probe with the target nucleic acid brings the azide and the alkyne in close proximity, allowing for ligation via bioorthogonal click chemistry (CuAAC). As a result, the probe forms a closed loop around the target sequence, thus enabling stringent washes to remove nonspecific binding in further rounds of amplification and retention of signal throughout liquid handling steps. Iterative rounds of hybridization with C-shaped, fluorescently labeled probes exponentially amplify the fluorescent signal. ClampFISH is simple to implement and expands the utility of in situ hybridization for multiple high throughput techniques such as low magnification microscopy, flow cytometry, and sorting based on RNA expression levels.