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This study evaluated the cellular heterogeneity and molecular mechanisms of hepatocellular carcinoma (HCC). Single cell RNA sequencing (scRNA-seq), transcriptomic data, histone lactylation-related genes were collected from public databases. Cell-cell interaction, trajectory, pathway, and spatial transcriptome analyses were executed. Differential expression and survival analyses were conducted. Western blot, Real-time reverse transcription PCR (qRT-PCR), and Cell Counting Kit 8 (CCK8) assay were used to detect the expression of αSMA, AKR1B10 and its target genes, and verify the roles of AKR1B10 in HCC cells. Hepatic stellate cell (HSC) subgroups strongly interacted with tumor cell subgroups, and their spatial distribution was heterogeneous. Two candidate prognostic genes (AKR1B10 and RMRP) were obtained. LONP1, NPIPB3, and ZSWIM6 were determined as AKR1B10 targets. Besides, the expression levels of AKR1B10 and αSMA were significantly increased in LX-2 + HepG2 and LX-2 + HuH7 groups compared to those in LX-2 group, respectively. sh-AKR1B10 significantly inhibited the HCC cell proliferation and change the expression of AKR1B10 target genes, Bcl-2, Bax, Pan Kla, and H3K18la at protein levels. Our findings unveil the pivotal role of HSCs in HCC pathogenesis through regulating histone lactylation.

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Recent studies have explored the spatial transcriptomics patterns of Alzheimer's disease (AD) brain by spatial sequencing in mouse models, enabling the identification of unique genome-wide transcriptomic features associated with different spatial regions and pathological status. However, the dynamics of gene interactions that occur during amyloid-ß accumulation remain largely unknown. In this study, we performed analyses on ligand-receptor communication, transcription factor regulatory network, and spot-specific network to reveal the dependence and the dynamics of gene associations/interactions on spatial regions and pathological status with mouse and human brains. We first used a spatial transcriptomics dataset of the App NL-G-F knock-in AD and wild-type mouse model. We revealed 17 ligand-receptor pairs with opposite tendencies throughout the amyloid-ß accumulation process and showed the specific ligand-receptor interactions across the hippocampus layers at different extents of pathological changes. We then identified nerve function related transcription factors in the hippocampus and entorhinal cortex, as well as genes with different transcriptomic association degrees in AD versus wild-type mice. Finally, another independent spatial transcriptomics dataset from different AD mouse models and human single-nuclei RNA-seq data/AlzData database were used for validation. This is the first study to identify various gene associations throughout amyloid-ß accumulation based on spatial transcriptomics, establishing the foundations to reveal advanced and in-depth AD etiology from a novel perspective based on the comprehensive analyses of gene interactions that are spatio-temporal dependent.

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The development and progression of diseases in multicellular organisms unfold within the intricate three-dimensional body environment. Thus, to comprehensively understand the molecular mechanisms governing individual development and disease progression, precise acquisition of biological data, including genome, transcriptome, proteome, metabolome, and epigenome, with single-cell resolution and spatial information within the body's three-dimensional context, is essential. This foundational information serves as the basis for deciphering cellular and molecular mechanisms. Although single-cell multi-omics technology can provide biological information such as genome, transcriptome, proteome, metabolome, and epigenome with single-cell resolution, the sample preparation process leads to the loss of spatial information. Spatial multi-omics technology, however, facilitates the characterization of biological data, such as genome, transcriptome, proteome, metabolome, and epigenome in tissue samples, while retaining their spatial context. Consequently, these techniques significantly enhance our understanding of individual development and disease pathology. Currently, spatial multi-omics technology has played a vital role in elucidating various processes in tumor biology, including tumor occurrence, development, and metastasis, particularly in the realms of tumor immunity and the heterogeneity of the tumor microenvironment. Therefore, this article provides a comprehensive overview of spatial transcriptomics, spatial proteomics, and spatial metabolomics-related technologies and their application in research concerning esophageal cancer, gastric cancer, and colorectal cancer. The objective is to foster the research and implementation of spatial multi-omics technology in digestive tumor diseases. This review will provide new technical insights for molecular biology researchers.

Asunto(s)

Neoplasias Gastrointestinales , Metabolómica , Proteómica , Humanos , Neoplasias Gastrointestinales/genética , Neoplasias Gastrointestinales/patología , Neoplasias Gastrointestinales/metabolismo , Genómica/métodos , Microambiente Tumoral , Transcriptoma , Multiómica

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Recent advancements in Spatial Transcriptomics (ST) have significantly enhanced biological research in various domains. However, the high cost of current ST data generation techniques restricts its application in large-scale population studies. Consequently, there is a pressing need to maximize the use of available resources to achieve robust statistical power. One fundamental question in ST analysis is to detect differentially expressed genes (DEGs) among different conditions using ST data. Such DEG analysis is often performed but the associated power calculation is rarely discussed in the literature. To address this gap, we introduce, PoweREST (https://github.com/lanshui98/PoweREST), a power estimation tool designed to support power calculation of DEG detection with 10X Genomics Visium data. PoweREST enables power estimation both before any ST experiments or after preliminary data are collected, making it suitable for a wide variety of power analyses in ST studies. We also provide a user-friendly, program-free web application (https://lanshui.shinyapps.io/PoweREST/), allowing users to interactively calculate and visualize the study power along with relevant the parameters.

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BACKGROUND: Temporal lobe epilepsy (TLE) is among the most common types of epilepsy and often leads to cognitive, emotional, and psychiatric issues due to the frequent seizures. A notable pathological change related to TLE is hippocampal sclerosis (HS), which is characterized by neuronal loss, gliosis, and an increased neuron fibre density. The mechanisms underlying TLE-HS development remain unclear, but the reactive transcriptomic changes in glial cells and neurons of the hippocampus post-epileptogenesis may provide insights. METHODS: To induce TLE, 200 nl of kainic acid (KA) was stereotactically injected into the hippocampal CA1 region of mice, followed by a 7-day postinjection period. Single-cell RNA sequencing (ScRNA-seq), single-nucleus RNA sequencing (SnRNA-seq), and Xenium-based spatial transcriptomics analyses were employed to evaluate the changes in mRNA expression in glial cells and neurons. RESULTS: From the ScRNA-seq and SnRNA-seq data, 31,390 glial cells and 48,221 neuronal nuclei were identified. Analysis of the differentially expressed genes (DEGs) revealed significant transcriptomic alterations in the hippocampal cells of mice with TLE, affecting hundreds to thousands of mRNAs and their signalling pathways. Enrichment analysis indicated notable activation of stress and inflammatory pathways in the TLE hippocampus, while pathways related to axonal development and neural support were suppressed. Xenium analysis demonstrated the expression of all 247 genes across mouse brain sections, revealing the spatial distributions of their expression in 27 cell types. Integrated analysis of the DEGs identified via the three sequencing techniques revealed that Spp1, Trem2, and Cd68 were upregulated in all glial cell types and in the Xenium data; Penk, Sorcs3, and Plekha2 were upregulated in all neuronal cell types and in the Xenium data; and Tle4 and Sipa1l3 were downregulated in all glial cell types and in the Xenium data. CONCLUSION: In this study, a high-resolution single-cell transcriptomic atlas of the hippocampus in mice with TLE was established, revealing potential intrinsic mechanisms driving TLE-associated inflammatory activation and altered cell interactions. These findings provide valuable insights for further exploration of HS development and epileptogenesis.

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Spatial transcriptomics (ST) examines gene expression within its spatial context on tissue, linking morphology and function. Advances in ST resolution and throughput have led to an increase in scientific interest, notably in cancer research. This scoping study reviews the challenges and practical applications of ST, summarizing current methods, trends, and data analysis techniques for ST in neoplasm research. We analyzed 41 articles published by the end of 2023 alongside public data repositories. The findings indicate cancer biology is an important focus of ST research, with a rising number of studies each year. Visium (10x Genomics, Pleasanton, CA, USA) is the leading ST platform, and SCTransform from Seurat R library is the preferred method for data normalization and integration. Many studies incorporate additional data types like single-cell sequencing and immunohistochemistry. Common ST applications include discovering the composition and function of tumor tissues in the context of their heterogeneity, characterizing the tumor microenvironment, or identifying interactions between cells, including spatial patterns of expression and co-occurrence. However, nearly half of the studies lacked comprehensive data processing protocols, hindering their reproducibility. By recommending greater transparency in sharing analysis methods and adapting single-cell analysis techniques with caution, this review aims to improve the reproducibility and reliability of future studies in cancer research.

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Juvenile localized and systemic scleroderma are rare autoimmune diseases which cause significant disability and morbidity in children. The mechanisms driving juvenile scleroderma remain unclear, necessitating further cellular and molecular level studies. The Visium CytAssist spatial transcriptomics (ST) platform, which preserves the spatial location of cells and simultaneously sequences the whole transcriptome, was employed to profile the histopathological slides from skin lesions of juvenile scleroderma patients. (1) Spatial domains were identified from ST data and exhibited strong concordance with the pathologist's annotations of anatomical structures. (2) The integration of paired ST data and single-cell RNA sequencing (scRNA-seq) from the same patients validated the comparable accuracy of the two platforms and facilitated the estimation of cell type composition in ST data. (3) The pathologist-annotated immune infiltrates, such as perivascular immune infiltrates, were clearly delineated by the ST analysis, underscoring the biological relevance of the findings. This is the first study utilizing spatial transcriptomics to investigate skin lesions in juvenile scleroderma patients. The validity of the ST data was corroborated by gene expression analyses and the pathologist's assessments. Integration with scRNA-seq data facilitated the cell type-level analysis and validation. Analyses of immune infiltrates through combined ST data and pathological review enhances our understanding of the pathogenesis of juvenile scleroderma.

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Perfilación de la Expresión Génica , Esclerodermia Sistémica , Piel , Transcriptoma , Humanos , Niño , Piel/patología , Piel/metabolismo , Proyectos Piloto , Esclerodermia Sistémica/genética , Esclerodermia Sistémica/patología , Esclerodermia Sistémica/metabolismo , Femenino , Masculino , Adolescente , Esclerodermia Localizada/genética , Esclerodermia Localizada/patología , Esclerodermia Localizada/metabolismo , Análisis de la Célula Individual , Preescolar , Análisis de Secuencia de ARN

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Traumatic spinal cord injury (tSCI) is a severe injury to the central nervous system that is categorized into primary and secondary injuries. Among them, the local microenvironmental imbalance in the spinal cord caused by secondary spinal cord injury includes accumulation of cytokines and chemokines, reduced angiogenesis, dysregulation of cellular energy metabolism, and dysfunction of immune cells at the site of injury, which severely impedes neurological recovery from spinal cord injury (SCI). In recent years, single-cell techniques have revealed the heterogeneity of multiple immune cells at the genomic, transcriptomic, proteomic, and metabolomic levels after tSCI, further deepening our understanding of the mechanisms underlying tSCI. However, spatial information about the tSCI microenvironment, such as cell location and cell-cell interactions, is lost in these approaches. The application of spatial multi-omics technology can solve this problem by combining the data obtained from immunohistochemistry and multiparametric analysis to reveal the changes in the microenvironment at different times of secondary injury after SCI. In this review, we systematically review the progress of spatial multi-omics techniques in the study of the microenvironment after SCI, including changes in the immune microenvironment and discuss potential future therapeutic strategies.

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Microambiente Celular , Proteómica , Traumatismos de la Médula Espinal , Traumatismos de la Médula Espinal/inmunología , Traumatismos de la Médula Espinal/metabolismo , Humanos , Microambiente Celular/inmunología , Proteómica/métodos , Animales , Metabolómica/métodos , Genómica/métodos , Transcriptoma , Análisis de la Célula Individual , Multiómica

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Spatial regulation of RNA plays a critical role in gene expression regulation and cellular function. Understanding spatially resolved RNA dynamics and translation is vital for bringing new insights into biological processes such as embryonic development, neurobiology, and disease pathology. This review explores past studies in subcellular, cellular, and tissue-level spatial RNA biology driven by diverse methodologies, ranging from cell fractionation, in situ and proximity labeling, imaging, spatially indexed next-generation sequencing (NGS) approaches, and spatially informed computational modeling. Particularly, recent advances have been made for near-genome-scale profiling of RNA and multimodal biomolecules at high spatial resolution. These methods enabled new discoveries into RNA's spatiotemporal kinetics, RNA processing, translation status, and RNA-protein interactions in cells and tissues. The evolving landscape of experimental and computational strategies reveals the complexity and heterogeneity of spatial RNA biology with subcellular resolution, heralding new avenues for RNA biology research.

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Cancer is a complex disease intrinsically associated with cellular processes and gene expression. With the development of techniques such as single-cell sequencing and sequential fluorescence in situ hybridization (seqFISH), it was possible to map the location of cells based on their gene expression with more precision. Moreover, in recent years, many tools have been developed to analyze these extensive datasets by integrating machine learning and artificial intelligence in a comprehensive manner. Since these tools analyze sequencing data, they offer the chance to analyze any tissue regardless of its origin. By applying this to cancer settings, spatial transcriptomic analysis based on artificial intelligence may help us understand cell-cell communications within the tumor microenvironment. Another advantage of this analysis is the identification of new biomarkers and therapeutic targets. The integration of such analysis with other omics data and with routine exams such as magnetic resonance imaging can help physicians with the earlier diagnosis of tumors as well as establish a more personalized treatment for pancreatic cancer patients. In this review, we give an overview description of pancreatic cancer, describe how spatial transcriptomics and artificial intelligence have been used to study pancreatic cancer and provide examples of how integrating these tools may help physicians manage pancreatic cancer in a more personalized approach.

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Inteligencia Artificial , Neoplasias Pancreáticas , Transcriptoma , Humanos , Neoplasias Pancreáticas/genética , Neoplasias Pancreáticas/patología , Neoplasias Pancreáticas/terapia , Transcriptoma/genética , Perfilación de la Expresión Génica/métodos , Biomarcadores de Tumor/genética , Microambiente Tumoral/genética , Manejo de la Enfermedad , Aprendizaje Automático

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Cancer is a dynamic disease, and clonal heterogeneity plays a fundamental role in tumor development, progression, and resistance to therapies. Single-cell and spatial multimodal technologies can provide a high-resolution molecular map of underlying genomic, epigenomic, and transcriptomic alterations involved in inter- and intra-tumor heterogeneity and interactions with the microenvironment. In this review, we provide a perspective on factors driving cancer heterogeneity, tumor evolution, and clonal states. We briefly describe spatial transcriptomic technologies and summarize recent literature that sheds light on the dynamical interactions between tumor states, cell-to-cell communication, and remodeling local microenvironment.

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Neoplasias , Transcriptoma , Microambiente Tumoral , Microambiente Tumoral/genética , Humanos , Neoplasias/genética , Neoplasias/patología , Transcriptoma/genética , Animales , Perfilación de la Expresión Génica/métodos , Regulación Neoplásica de la Expresión Génica , Comunicación Celular/genética , Análisis de la Célula Individual/métodos

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The advent of deep learning (DL) and multimodal spatial transcriptomics (ST) has revolutionized cancer research, offering unprecedented insights into tumor biology. This book chapter explores the integration of DL with ST to advance cancer diagnostics, treatment planning, and precision medicine. DL, a subset of artificial intelligence, employs neural networks to model complex patterns in vast datasets, significantly enhancing diagnostic and treatment applications. In oncology, convolutional neural networks excel in image classification, segmentation, and tumor volume analysis, essential for identifying tumors and optimizing radiotherapy. The chapter also delves into multimodal data analysis, which integrates genomic, proteomic, imaging, and clinical data to offer a holistic understanding of cancer biology. Leveraging diverse data sources, researchers can uncover intricate details of tumor heterogeneity, microenvironment interactions, and treatment responses. Examples include integrating MRI data with genomic profiles for accurate glioma grading and combining proteomic and clinical data to uncover drug resistance mechanisms. DL's integration with multimodal data enables comprehensive and actionable insights for cancer diagnosis and treatment. The synergy between DL models and multimodal data analysis enhances diagnostic accuracy, personalized treatment planning, and prognostic modeling. Notable applications include ST, which maps gene expression patterns within tissue contexts, providing critical insights into tumor heterogeneity and potential therapeutic targets. In summary, the integration of DL and multimodal ST represents a paradigm shift towards more precise and personalized oncology. This chapter elucidates the methodologies and applications of these advanced technologies, highlighting their transformative potential in cancer research and clinical practice.

Asunto(s)

Aprendizaje Profundo , Neoplasias , Humanos , Neoplasias/genética , Neoplasias/patología , Neoplasias/diagnóstico , Transcriptoma/genética , Perfilación de la Expresión Génica/métodos , Medicina de Precisión/métodos

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Cells in multicellular organisms constitute a self-organizing society by interacting with their neighbors. Cancer originates from malfunction of cellular behavior in the context of such a self-organizing system. The identities or characteristics of individual tumor cells can be represented by the hallmark of gene expression or transcriptome, which can be addressed using single-cell dissociation followed by RNA sequencing. However, the dissociation process of single cells results in losing the cellular address in tissue or neighbor information of each tumor cell, which is critical to understanding the malfunctioning cellular behavior in the microenvironment. Spatial transcriptomics technology enables measuring the transcriptome which is tagged by the address within a tissue. However, to understand cellular behavior in a self-organizing society, we need to apply mathematical or statistical methods. Here, we provide a review on current computational methods for spatial transcriptomics in cancer biology.

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Biología Computacional , Neoplasias , Transcriptoma , Humanos , Neoplasias/genética , Neoplasias/patología , Transcriptoma/genética , Biología Computacional/métodos , Perfilación de la Expresión Génica/métodos , Microambiente Tumoral/genética , Animales

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Unveiling the intricate interplay of cells in their native environment lies at the heart of understanding fundamental biological processes and unraveling disease mechanisms, particularly in complex diseases like cancer. Spatial transcriptomics (ST) offers a revolutionary lens into the spatial organization of gene expression within tissues, empowering researchers to study both cell heterogeneity and microenvironments in health and disease. However, current ST technologies often face limitations in either resolution or the number of genes profiled simultaneously. Integrating ST data with complementary sources, such as single-cell transcriptomics and detailed tissue staining images, presents a powerful solution to overcome these limitations. This review delves into the computational approaches driving the integration of spatial transcriptomics with other data types. By illuminating the key challenges and outlining the current algorithmic solutions, we aim to highlight the immense potential of these methods to revolutionize our understanding of cancer biology.

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Neoplasias , Humanos , Neoplasias/patología , Neoplasias/genética , Biología Computacional/métodos , Perfilación de la Expresión Génica/métodos , Transcriptoma , Análisis de la Célula Individual/métodos , Animales , Microambiente Tumoral , Algoritmos

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BACKGROUND: The tumor microenvironment (TME) exerts profound effects on tumor progression and therapeutic efficacy. In hepatocellular carcinoma (HCC), the TME is enriched with cancer-associated fibroblasts (CAFs), which secrete a plethora of cytokines, chemokines, and growth factors that facilitate tumor cell proliferation and invasion. However, the intricate architecture of the TME in HCC, as well as the mechanisms driving interactions between tumor cells and CAFs, remains largely enigmatic. METHODS: We analyzed 10 spatial transcriptomics and 12 single-cell transcriptomics samples sourced from public databases, complemented by 20 tumor tissue samples from liver cancer patients obtained in a clinical setting. RESULTS: Our findings reveal that tumor cells exhibiting high levels of SPP1 are preferentially localized adjacent to hepatic stellate cells (HSCs). The SPP1 secreted by these tumor cells interacts with the CD44 receptor on HSCs, thereby activating the PI3K/AKT signaling pathway, which promotes the differentiation of HSCs into CAFs. Notably, blockade of the CD44 receptor effectively abrogates this interaction. Furthermore, in vivo studies demonstrate that silencing SPP1 expression in tumor cells significantly impairs HSC differentiation into CAFs, leading to a reduction in tumor volume and collagen deposition within the tumor stroma. CONCLUSIONS: This study delineates the SPP1-CD44 signaling axis as a pivotal mechanism underpinning the interaction between tumor cells and CAFs. Targeting this pathway holds potential to mitigate liver fibrosis and offers novel therapeutic perspectives for liver cancer management.

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Carcinoma Hepatocelular , Quimiotaxis , Células Estrelladas Hepáticas , Neoplasias Hepáticas , Transcriptoma , Microambiente Tumoral , Carcinoma Hepatocelular/patología , Carcinoma Hepatocelular/genética , Carcinoma Hepatocelular/metabolismo , Neoplasias Hepáticas/patología , Neoplasias Hepáticas/genética , Neoplasias Hepáticas/metabolismo , Humanos , Transcriptoma/genética , Células Estrelladas Hepáticas/metabolismo , Células Estrelladas Hepáticas/patología , Animales , Quimiotaxis/genética , Fibroblastos/metabolismo , Fibroblastos/patología , Línea Celular Tumoral , Transducción de Señal , Receptores de Hialuranos/metabolismo , Fibroblastos Asociados al Cáncer/metabolismo , Fibroblastos Asociados al Cáncer/patología , Diferenciación Celular , Proteínas Proto-Oncogénicas c-akt/metabolismo , Fosfatidilinositol 3-Quinasas/metabolismo , Regulación Neoplásica de la Expresión Génica

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BACKGROUND: Multiple studies have shown that tumor-associated macrophages (TAMs) promote cancer initiation and progression. However, the reprogramming of macrophages in the tumor microenvironment (TME) and the cross-talk between TAMs and malignant subclones in intrahepatic cholangiocarcinoma (iCCA) has not been fully characterized, especially in a spatially resolved manner. Deciphering the spatial architecture of variable tissue cellular components in iCCA could contribute to the positional context of gene expression containing information pathological changes and cellular variability. METHODS: Here, we applied spatial transcriptomics (ST) and digital spatial profiler (DSP) technologies with tumor sections from patients with iCCA. RESULTS: The results reveal that spatial inter- and intra-tumor heterogeneities feature iCCA malignancy, and tumor subclones are mainly driven by physical proximity. Tumor cells with TME components shaped the intra-sectional heterogenetic spatial architecture. Macrophages are the most infiltrated TME component in iCCA. The protein trefoil factor 3 (TFF3) secreted by the malignant subclone can induce macrophages to reprogram to a tumor-promoting state, which in turn contributes to an immune-suppressive environment and boosts tumor progression. CONCLUSIONS: In conclusion, our description of the iCCA ecosystem in a spatially resolved manner provides novel insights into the spatial features and the immune suppressive landscapes of TME for iCCA.

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Background: Glioma is a highly heterogeneous malignancy of the central nervous system. This heterogeneity is driven by various molecular processes, including neoplastic transformation, cell cycle dysregulation, and angiogenesis. Among these biomolecular events, inflammation and stress pathways in the development and driving factors of glioma heterogeneity have been reported. However, the mechanisms of glioma heterogeneity under stress response remain unclear, especially from a spatial aspect. Methods: This study employed single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) to explore the impact of oxidative stress response genes in oligodendrocyte precursor cells (OPCs). Our analysis identified distinct pathways activated by oxidative stress in two different types of gliomas: high- and low- grade (HG and LG) gliomas. Results: In HG gliomas, oxidative stress induced a metabolic shift from oxidative phosphorylation to glycolysis, promoting cell survival by preventing apoptosis. This metabolic reprogramming was accompanied by epithelial-to-mesenchymal transition (EMT) and an upregulation of stress response genes. Furthermore, SCENIC (Single-Cell rEgulatory Network Inference and Clustering) analysis revealed that oxidative stress activated the AP1 transcription factor in HG gliomas, thereby enhancing tumor cell survival and proliferation. Conclusion: Our findings provide a novel perspective on the mechanisms of oxidative stress responses across various grades of gliomas. This insight enhances our comprehension of the evolutionary processes and heterogeneity within gliomas, potentially guiding future research and therapeutic strategies.

Asunto(s)

Neoplasias Encefálicas , Glioma , Estrés Oxidativo , Análisis de la Célula Individual , Transcriptoma , Glioma/genética , Glioma/patología , Glioma/metabolismo , Animales , Neoplasias Encefálicas/genética , Neoplasias Encefálicas/patología , Neoplasias Encefálicas/metabolismo , Humanos , Transición Epitelial-Mesenquimal/genética , Regulación Neoplásica de la Expresión Génica , Células Precursoras de Oligodendrocitos/metabolismo , Perfilación de la Expresión Génica , Transducción de Señal , Proliferación Celular/genética , Línea Celular Tumoral , Redes Reguladoras de Genes

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The emergence of spatial profiling technologies in recent years has accelerated opportunities to profile in detail the molecular attributes of a wide range of tissue pathologies using archival specimens. However, tissue treatment for fixation and storage does not always support generation of high-quality genomic data. The purpose of this study was to investigate the impacts of Proteinase K (ProtK) treatment, as a way to increase target transcript exposure, on downstream sequencing data quality metrics for spatial transcriptomic data using formalin-fixed, paraffin-embedded samples. In a series of four independent assessments using different tissue types (nasal mucosa, tonsil, pancreas), varying concentrations of ProtK (ranging from 0.1 to 1 µg/mL) were used as part of the sample processing workflow to generate transcriptomic data using the Nanostring GeoMx DSP and Illumina NextSeq 2000 platforms. Use of higher concentrations of ProtK was generally found to increase total reads (2-4-fold). However, negative probe counts also tended to be increased (2-12-fold), resulting in reductions in the signal-to-noise ratio (10-70% lower) and the number of genes detected above background (50-80% lower). These effects were not seen in all tissues and impacts of tissue handling and processing, beyond ProtK treatment, on data quality metrics, also require consideration. Regardless, these observations highlight the need for careful consideration of a range of sample processing factors and benefits that may be achieved through the optimisation of sample processing workflows for specific tissues as a way to maximise the generation of quality data using spatial transcriptomic approaches.

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Recent advances in spatial transcriptomics have significantly deepened our understanding of biology. A primary focus has been identifying spatially variable genes (SVGs) which are crucial for downstream tasks like spatial domain detection. Traditional methods often use all or a set number of top SVGs for this purpose. However, in diverse datasets with many SVGs, this approach may not ensure accurate results. Instead, grouping SVGs by expression patterns and using all SVG groups in downstream analysis can improve accuracy. Furthermore, classifying SVGs in this manner is akin to identifying cell type marker genes, offering valuable biological insights. The challenge lies in accurately categorizing SVGs into relevant clusters, aggravated by the absence of prior knowledge regarding the number and spectrum of spatial gene patterns. Addressing this challenge, we propose SPACE, SPatially variable gene clustering Adjusting for Cell type Effect, a framework that classifies SVGs based on their spatial patterns by adjusting for confounding effects caused by shared cell types, to improve spatial domain detection. This method does not require prior knowledge of gene cluster numbers, spatial patterns, or cell type information. Our comprehensive simulations and real data analyses demonstrate that SPACE is an efficient and promising tool for spatial transcriptomics analysis.