Spatial transcriptomic interrogation of the tumour-stroma boundary in a 3D engineered model of ameloblastoma

Stromal cells are key components of the tumour microenvironment (TME) and their incorporation into 3D engineered tumour-stroma models is essential for tumour mimicry. By engineering tumouroids with distinct tumour and stromal compartments, it has been possible to identify how gene expression of tumour cells is altered and influenced by the presence of different stromal cells. Ameloblastoma is a benign epithelial tumour of the jawbone. In engineered, multi-compartment tumouroids spatial transcriptomics revealed an upregulation of oncogenes in the ameloblastoma transcriptome where osteoblasts were present in the stromal compartment (bone stroma). Where a gingival fibroblast stroma was engineered, the ameloblastoma tumour transcriptome revealed increased matrix remodelling genes. This study provides evidence to show the stromal-specific effect on tumour behaviour and illustrates the importance of engineering biologically relevant stroma for engineered tumour models. Our novel results show that an engineered fibroblast stroma causes the upregulation of matrix remodelling genes in ameloblastoma which directly correlates to measured invasion in the model. In contrast the presence of a bone stroma increases the expression of oncogenes by ameloblastoma cells.

Cellular heterogeneity in red and melanized focal muscle changes in farmed Atlantic salmon (Salmo salar) visualized by spatial transcriptomics

Spatial transcriptomics is a technique that provides insight into gene expression profiles in tissue sections while retaining structural information. We have employed this method to study the pathological conditions related to red and melanized focal changes in farmed Atlantic salmon (Salmo salar). Our findings support a model where similar molecular mechanisms are involved in both red and melanized filet discolorations and genes associated with several relevant pathways show distinct expression patterns in both sample types. Interestingly, there appears to be significant cellular heterogeneity in the foci investigated when looking at gene expression patterns. Some of the genes that show differential spatial expression are involved in cellular processes such as hypoxia and immune responses, providing new insight into the nature of muscle melanization in Atlantic salmon.

Spatial multimodal analysis revealed tertiary lymphoid structures as a risk stratification indicator in combined hepatocellular-cholangiocarcinoma

The microenvironment created by tertiary lymphoid structures (TLSs) can support and regulate immune responses, affecting the prognosis and immune treatment of patients. Nevertheless, the actual importance of TLSs for predicting the prognosis of combined hepatocellular-cholangiocarcinoma (cHCC-CCA) patients remains unclear. Herein, using spatial transcriptomic analysis, we revealed that a gene signature of TLSs specific to cHCC-CCA was associated with high-intensity immune infiltration. Then, a novel scoring system was developed to evaluate the distribution and frequency of TLSs in intra-tumoral and extra-tumoral regions (iTLS and eTLS scores) in 146 cHCC-CCA patients. iTLS score was positively associated with promising prognosis, likely due to the decreased frequency of suppressive immune cell like Tregs, and the ratio of CD163+ macrophages to macrophages in intra-tumoral TLSs via imaging mass cytometry, while improved prognosis is not necessarily indicated by a higher eTLS score. Overall, this study highlights the potential of TLSs as a prognostic factor and an indicator of immune therapy in cHCC-CCA.

Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases

Spatial multi-omic studies have emerged as a promising approach to comprehensively analyze cells in tissues, enabling the joint analysis of multiple data modalities like transcriptome, epigenome, proteome, and metabolome in parallel or even the same tissue section. This review focuses on the recent advancements in spatial multi-omics technologies, including novel data modalities and computational approaches. We discuss the advancements in low-resolution and high-resolution spatial multi-omics methods which can resolve up to 10,000 of individual molecules at subcellular level. By applying and integrating these techniques, researchers have recently gained valuable insights into the molecular circuits and mechanisms which govern cell biology along the cardiovascular disease spectrum. We provide an overview of current data analysis approaches, with a focus on data integration of multi-omic datasets, highlighting strengths and weaknesses of various computational pipelines. These tools play a crucial role in analyzing and interpreting spatial multi-omics datasets, facilitating the discovery of new findings, and enhancing translational cardiovascular research. Despite nontrivial challenges, such as the need for standardization of experimental setups, data analysis, and improved computational tools, the application of spatial multi-omics holds tremendous potential in revolutionizing our understanding of human disease processes and the identification of novel biomarkers and therapeutic targets. Exciting opportunities lie ahead for the spatial multi-omics field and will likely contribute to the advancement of personalized medicine for cardiovascular diseases.

Mapping the single cell spatial immune landscapes of the melanoma microenvironment

Melanoma is a highly immunogenic malignancy with an elevated mutational burden, diffuse lymphocytic infiltration, and one of the highest response rates to immune checkpoint inhibitors (ICIs). However, over half of all late-stage patients treated with ICIs will either not respond or develop progressive disease. Spatial imaging technologies are being increasingly used to study the melanoma tumor microenvironment (TME). The goal of such studies is to understand the complex interplay between the stroma, melanoma cells, and immune cell-types as well as their association with treatment response. Investigators seeking a better understanding of the role of cell location within the TME and the importance of spatial expression of biomarkers are increasingly turning to highly multiplexed imaging approaches to more accurately measure immune infiltration as well as to quantify receptor-ligand interactions (such as PD-1 and PD-L1) and cell-cell contacts. CyTOF-IMC (Cytometry by Time of Flight - Imaging Mass Cytometry) has enabled high-dimensional profiling of melanomas, allowing researchers to identify complex cellular subpopulations and immune cell interactions with unprecedented resolution. Other spatial imaging technologies, such as multiplexed immunofluorescence and spatial transcriptomics, have revealed distinct patterns of immune cell infiltration, highlighting the importance of spatial relationships, and their impact in modulating immunotherapy responses. Overall, spatial imaging technologies are just beginning to transform our understanding of melanoma biology, providing new avenues for biomarker discovery and therapeutic development. These technologies hold great promise for advancing personalized medicine to improve patient outcomes in melanoma and other solid malignancies.

Unsupervised spatially embedded deep representation of spatial transcriptomics

Optimal integration of transcriptomics data and associated spatial information is essential towards fully exploiting spatial transcriptomics to dissect tissue heterogeneity and map out inter-cellular communications. We present SEDR, which uses a deep autoencoder coupled with a masked self-supervised learning mechanism to construct a low-dimensional latent representation of gene expression, which is then simultaneously embedded with the corresponding spatial information through a variational graph autoencoder. SEDR achieved higher clustering performance on manually annotated 10 × Visium datasets and better scalability on high-resolution spatial transcriptomics datasets than existing methods. Additionally, we show SEDR's ability to impute and denoise gene expression (URL: https://github.com/JinmiaoChenLab/SEDR/ ).

Profibrotic Subsets of SPP1+ Macrophages and POSTN+ Fibroblasts Contribute to Fibrotic Scarring in Acne Keloidalis

Acne keloidalis is a primary scarring alopecia characterized by longstanding inflammation in the scalp causing keloid-like scar formation and hair loss. Histologically, acne keloidalis is characterized by mixed leukocytic infiltrates in the acute stage followed by a granulomatous reaction and extensive fibrosis in the later stages. To further explore its pathogenesis, bulk RNA sequencing, single-cell RNA sequencing, and spatial transcriptomics were applied to occipital scalp biopsy specimens of lesional and adjacent no-lesional skin in patients with clinically active disease. Unbiased clustering revealed 19 distinct cell populations, including 2 notable populations: POSTN+ fibroblasts with enriched extracellular matrix signatures and SPP1+ myeloid cells with an M2 macrophage phenotype. Cell communication analyses indicated that fibroblasts and myeloid cells communicated by SPP1 signaling networks in lesional skin. A reverse transcriptomics in silico approach identified corticosteroids as possessing the capability to reverse the gene expression signatures of SPP1+ myeloid cells and POSTN+ fibroblasts. Intralesional corticosteroid injection greatly reduced SPP1 and POSTN gene expression as well as acne keloidalis disease activity. Spatial transcriptomics and immunofluorescence staining verified microanatomic specificity of SPP1+ myeloid cells and POSTN+ fibroblasts with disease activity. In summary, the communication between POSTN+ fibroblasts and SPP1+ myeloid cells by SPP1 axis may contribute to the pathogenesis of acne keloidalis.

Cell-type brain-region specific changes in prefrontal cortex of a mouse model of alcohol dependence

The prefrontal cortex is a crucial regulator of alcohol drinking, and dependence, and other behavioral phenotypes associated with AUD. Comprehensive identification of cell-type specific transcriptomic changes in alcohol dependence will improve our understanding of mechanisms underlying the excessive alcohol use associated with alcohol dependence and will refine targets for therapeutic development. We performed single nucleus RNA sequencing (snRNA-seq) and Visium spatial gene expression profiling on the medial prefrontal cortex (mPFC) obtained from C57BL/6 J mice exposed to the two-bottle choice-chronic intermittent ethanol (CIE) vapor exposure (2BC-CIE, defined as dependent group) paradigm which models phenotypes of alcohol dependence including escalation of alcohol drinking. Gene co-expression network analysis and differential expression analysis identified highly dysregulated co-expression networks in multiple cell types. Dysregulated modules and their hub genes suggest novel understudied targets for studying molecular mechanisms contributing to the alcohol dependence state. A subtype of inhibitory neurons was the most alcohol-sensitive cell type and contained a downregulated gene co-expression module; the hub gene for this module is Cpa6, a gene previously identified by GWAS to be associated with excessive alcohol consumption. We identified an astrocytic Gpc5 module significantly upregulated in the alcohol-dependent group. To our knowledge, there are no studies linking Cpa6 and Gpc5 to the alcohol-dependent phenotype. We also identified neuroinflammation related gene expression changes in multiple cell types, specifically enriched in microglia, further implicating neuroinflammation in the escalation of alcohol drinking. Here, we present a comprehensive atlas of cell-type specific alcohol dependence mediated gene expression changes in the mPFC and identify novel cell type-specific targets implicated in alcohol dependence.

Challenges and Opportunities for the Clinical Translation of Spatial Transcriptomics Technologies

Background

The first spatially resolved transcriptomics platforms, GeoMx (Nanostring) and Visium (10x Genomics) were launched in 2019 and were recognized as the method of the year by Nature Methods in 2020. The subsequent refinement and expansion of these and other technologies to increase -plex, work with formalin-fixed paraffin-embedded tissue, and analyze protein in addition to gene expression have only added to their significance and impact on the biomedical sciences. In this perspective, we focus on two platforms for spatial transcriptomics, GeoMx and Visium, and how these platforms have been used to provide novel insight into kidney disease. The choice of platform will depend largely on experimental questions and design. The application of these technologies to clinically sourced biopsies presents the opportunity to identify specific tissue biomarkers that help define disease etiology and more precisely target therapeutic interventions in the future.

Summary

In this review, we provide a description of the existing and emerging technologies that can be used to capture spatially resolved gene and protein expression data from tissue. These technologies have provided new insight into the spatial heterogeneity of diseases, how reactions to disease are distributed within a tissue, which cells are affected, and molecular pathways that predict disease and response to therapy.

Key Message

The upcoming years will see intense use of spatial transcriptomics technologies to better define the pathophysiology of kidney diseases and develop novel diagnostic tests to guide personalized treatments for patients.

Targeting stroma and tumor, silencing galectin 1 treats orthotopic mouse hepatocellular carcinoma

This study examines inhibiting galectin 1 (Gal1) as a treatment option for hepatocellular carcinoma (HCC). Gal1 has immunosuppressive and cancer-promoting roles. Our data showed that Gal1 was highly expressed in human and mouse HCC. The levels of Gal1 positively correlated with the stages of human HCC and negatively with survival. The roles of Gal1 in HCC were studied using overexpression (OE) or silencing using Igals1 siRNA delivered by AAV9. Prior to HCC initiation induced by RAS and AKT mutations, lgals1-OE and silencing had opposite impacts on tumor load. The treatment effect of lgals1 siRNA was further demonstrated by intersecting HCC at different time points when the tumor load had already reached 9% or even 42% of the body weight. Comparing spatial transcriptomic profiles of Gal1 silenced and OE HCC, inhibiting matrix formation and recognition of foreign antigen in CD45+ cell-enriched areas located at tumor-margin likely contributed to the anti-HCC effects of Gal1 silencing. Within the tumors, silencing Gal1 inhibited translational initiation, elongation, and termination. Furthermore, Gal1 silencing increased immune cells as well as expanded cytotoxic T cells within the tumor, and the anti-HCC effect of lgals1 siRNA was CD8-dependent. Overall, Gal1 silencing has a promising potential for HCC treatment.

Transformer with convolution and graph-node co-embedding: An accurate and interpretable vision backbone for predicting gene expressions from local histopathological image

Inferring gene expressions from histopathological images has long been a fascinating yet challenging task, primarily due to the substantial disparities between the two modality. Existing strategies using local or global features of histological images are suffering model complexity, GPU consumption, low interpretability, insufficient encoding of local features, and over-smooth prediction of gene expressions among neighboring sites. In this paper, we develop TCGN (Transformer with Convolution and Graph-Node co-embedding method) for gene expression estimation from H&E-stained pathological slide images. TCGN comprises a combination of convolutional layers, transformer encoders, and graph neural networks, and is the first to integrate these blocks in a general and interpretable computer vision backbone. Notably, TCGN uniquely operates with just a single spot image as input for histopathological image analysis, simplifying the process while maintaining interpretability. We validate TCGN on three publicly available spatial transcriptomic datasets. TCGN consistently exhibited the best performance (with median PCC 0.232). TCGN offers superior accuracy while keeping parameters to a minimum (just 86.241 million), and it consumes minimal memory, allowing it to run smoothly even on personal computers. Moreover, TCGN can be extended to handle bulk RNA-seq data while providing the interpretability. Enhancing the accuracy of omics information prediction from pathological images not only establishes a connection between genotype and phenotype, enabling the prediction of costly-to-measure biomarkers from affordable histopathological images, but also lays the groundwork for future multi-modal data modeling. Our results confirm that TCGN is a powerful tool for inferring gene expressions from histopathological images in precision health applications.

Comparative Methods for Demystifying Spatial Transcriptomics

Spatial Transcriptomics (ST), coined as the term for parallel RNA-Seq on cell populations ordered spatially on a histological tissue section, has recently become increasingly popular, especially in experiments where microfluidics-based single-cell sequencing fails, such as assays on neurons. ST platforms, like the 10x Visium technology investigated herein, therefore produce in a single experiment simultaneously thousands of RNA readouts, captured by an array of micrometer scale spots under the histological section. Therefore, a central challenge of analyzing ST experiments consists of analyzing the gene expression morphology of all spots to delineate clusters of similar cell mixtures, which are then compared to each other to identify up- or down-regulated marker genes. Moreover, another level of complexity in ST experiments, compared to traditional RNA-Seq, is imposed by staining the tissue section with protein markers of cells or cell components to identify spots providing relevant information afterward. The corresponding microscopy images need to be analyzed in addition to the RNA-Seq read mappings on the reference genome and transcriptome sequences. Focusing on the software suite provided by the Visium platform manufacturer, we break down the ST analysis pipeline into its four essential steps-the image analysis, the read alignment, the gene quantification, and the spot clustering-and compare results obtained when using reads from different subsets of spots and/or when employing alternative genome or transcriptome references. Our comparative analyses demonstrate the impact of spot selection and the choice of genome/transcriptome references on the analysis results when employing the manufacturer's pipeline.

Designing Oligonucleotide-Based FISH Probe Sets with PaintSHOP

Fluorescent in situ hybridization (FISH) enables the visualization of the position and abundance of nucleic acid molecules in fixed cell and tissue samples. Many FISH-based methods employ sets of synthetic, computationally designed DNA oligonucleotide (oligo) FISH probes, including massively multiplexed imaging spatial transcriptomics and spatial genomics technologies. Oligo probes can either be designed de novo or accessed from an existing database of pre-discovered probe sequences. This chapter describes the use of PaintSHOP, a user-friendly, web-based platform for the design of sets of oligo-based FISH probes. PaintSHOP hosts large collections of pre-discovered probes from many model organisms and also provides collections of functional sequences such as primers and readout domains and interactive tools to add these functional sequences to selected probes. Detailed examples are provided for three common experimental scenarios.

ENGEP: advancing spatial transcriptomics with accurate unmeasured gene expression prediction

Imaging-based spatial transcriptomics techniques provide valuable spatial and gene expression information at single-cell resolution. However, their current capability is restricted to profiling a limited number of genes per sample, resulting in most of the transcriptome remaining unmeasured. To overcome this challenge, we develop ENGEP, an ensemble learning-based tool that predicts unmeasured gene expression in spatial transcriptomics data by using multiple single-cell RNA sequencing datasets as references. ENGEP outperforms current state-of-the-art tools and brings biological insight by accurately predicting unmeasured genes. ENGEP has exceptional efficiency in terms of runtime and memory usage, making it scalable for analyzing large datasets.

Exploring distinct properties of endometrial stem cells through advanced single-cell analysis platforms

The endometrium is a dynamic tissue that undergoes cyclic changes in response to ovarian hormones during the menstrual cycle. These changes are crucial for pregnancy establishment and maintenance. Endometrial stem cells play a pivotal role in endometrial regeneration and repair by differentiating into various cell types within the endometrium. However, their involvement in endometrial disorders such as endometriosis, infertility, and endometrial cancer is still not fully understood yet. Traditional bulk sequencing methods have limitations in capturing heterogeneity and complexity of endometrial stem cell populations. To overcome these limitations, recent single-cell analysis techniques, including single-cell RNA sequencing (scRNA-Seq), single-cell ATAC sequencing (scATAC-Seq), and spatial transcriptomics, have emerged as valuable tools for studying endometrial stem cells. In this review, although there are still many technical limitations that require improvement, we will summarize the current state-of-the-art single-cell analysis techniques for endometrial stem cells and explore their relevance to related diseases. We will discuss studies utilizing various single-cell analysis platforms to identify and characterize distinct endometrial stem cell populations and investigate their dynamic changes in gene expression and epigenetic patterns during menstrual cycle and differentiation processes. These techniques enable the identification of rare cell populations, capture heterogeneity of cell populations within the endometrium, and provide potential targets for more effective therapies.

Unveiling the hidden AP-1: revealing the crucial role of AP-1 in ccRCC at single-cell resolution

Clear cell renal cell carcinoma (ccRCC), as the most common histological subtype of kidney cancer, has been reported to originate primarily from proximal tubule (PT) cells in the kidney. However, the current research on its associated molecular mechanisms remains relatively limited. In our study, we analyzed multiple single-cell multi-omics datasets obtained from various research teams, revealing the significant role of the activator protein 1 (AP-1) in ccRCC tumorigenesis. The motif activity analysis of transcription factors (TFs) showed a predominant activation of AP-1 in ccRCC cancer cells compared to PT cells. Furthermore, our findings at single-cell resolution revealed a notable absence of AP-1 expression in PT cells when compared to ccRCC cancer cells. In bulk-RNA of discovery cohort, no differential expression of AP-1 was detected in normal kidney and ccRCC samples, which may be attributed to confounding effects in bulk-RNA sequencing. Meanwhile, spatial transcriptomics analysis demonstrated a broader expression range of the AP-1 compared to the ccRCC marker CA9. Moreover, we observed chromatin accessibility of the AP-1 in various cell-types, including PT cells, suggesting that the transcriptional expression of AP-1 in PT cells may be influenced by subsequent transcriptional modifications, reflecting the complex regulatory mechanism of AP-1 transcription. These findings provide important insights for a deeper understanding of the function and regulatory mechanisms of AP-1 in ccRCC, thereby establishing a theoretical foundation for future clinical research and the development of treatment strategies.

Acute ischemia induces spatially and transcriptionally distinct microglial subclusters

BACKGROUND

Damage in the ischemic core and penumbra after stroke affects patient prognosis. Microglia immediately respond to ischemic insult and initiate immune inflammation, playing an important role in the cellular injury after stroke. However, the microglial heterogeneity and the mechanisms involved remain unclear.

METHODS

We first performed single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics (ST) on middle cerebral artery occlusion (MCAO) mice from three time points to determine stroke-associated microglial subclusters and their spatial distributions. Furthermore, the expression of microglial subcluster-specific marker genes and the localization of different microglial subclusters were verified on MCAO mice through RNAscope and immunofluorescence. Gene set variation analysis (GSVA) was performed to reveal functional characteristics of microglia sub-clusters. Additionally, ingenuity pathway analysis (IPA) was used to explore upstream regulators of microglial subclusters, which was confirmed by immunofluorescence, RT-qPCR, shRNA-mediated knockdown, and targeted metabolomics. Finally, the infarct size, neurological deficits, and neuronal apoptosis were evaluated in MCAO mice after manipulation of specific microglial subcluster.

RESULTS

We discovered stroke-associated microglial subclusters in the brains of MCAO mice. We also identified novel marker genes of these microglial subclusters and defined these cells as ischemic core-associated (ICAM) and ischemic penumbra-associated (IPAM) microglia, according to their spatial distribution. ICAM, induced by damage-associated molecular patterns, are probably fueled by glycolysis, and exhibit increased pro-inflammatory cytokines and chemokines production. BACH1 is a key transcription factor driving ICAM generation. In contrast, glucocorticoids, which are enriched in the penumbra, likely trigger IPAM formation, which are presumably powered by the citrate cycle and oxidative phosphorylation and are characterized by moderate pro-inflammatory responses, inflammation-alleviating metabolic features, and myelinotrophic properties.

CONCLUSIONS

ICAM could induce excessive neuroinflammation, aggravating brain injury, whereas IPAM probably exhibit neuroprotective features, which could be essential for the homeostasis and survival of cells in the penumbra. Our findings provide a biological basis for targeting specific microglial subclusters as a potential therapeutic strategy for ischemic stroke.

An interactive resource of molecular signalling in the developing human haematopoietic stem cell niche

The emergence of definitive human haematopoietic stem cells (HSCs) from Carnegie Stage (CS) 14 to CS17 in the aorta-gonad-mesonephros (AGM) region is a tightly regulated process. Previously, we conducted spatial transcriptomic analysis of the human AGM region at the end of this period (CS16/CS17) and identified secreted factors involved in HSC development. Here, we extend our analysis to investigate the progression of dorso-ventral polarised signalling around the dorsal aorta over the entire period of HSC emergence. Our results reveal a dramatic increase in ventral signalling complexity from the CS13-CS14 transition, coinciding with the first appearance of definitive HSCs. We further observe stage-specific changes in signalling up to CS17, which may underpin the step-wise maturation of HSCs described in the mouse model. The data-rich resource is also presented in an online interface enabling in silico analysis of molecular interactions between spatially defined domains of the AGM region. This resource will be of particular interest for researchers studying mechanisms underlying human HSC development as well as those developing in vitro methods for the generation of clinically relevant HSCs from pluripotent stem cells.

Hexagonal image segmentation on spatially resolved transcriptomics

Spatial transcriptomics is a rapidly evolving field that enables researchers to capture comprehensive molecular profiles while preserving information about the physical locations. One major challenge in this research area involves the identification of spatial domains, which are distinct regions characterized by unique gene expression patterns. However, current unsupervised methods have struggled to perform well in this regard due to the presence of high levels of noise and dropout events in spatial transcriptomic profiles. In this paper, we propose a novel hexagonal Convolutional Neural Network (hexCNN) for hexagonal image segmentation on spatially resolved transcriptomics. To address the problem of noise and dropout occurrences within spatial transcriptomics data, we first extend an unsupervised algorithm to a supervised learning method that can identify useful features and reduce noise hindrance. Then, inspired by the classical convolution in convolutional neural networks (CNNs), we designed a regular hexagonal convolution to compensate for the missing gene expression patterns from adjacent spots. We evaluated the performance of hexCNN by applying it to the DLPFC dataset. The results show that hexCNN achieves a classification accuracy of 86.8% and an average Rand index (ARI) of 77.1% (1.4% and 2.5% higher than those of GNNs). The results also demonstrate that hexCNN is capable of removing the noise caused by batch effect while preserving the biological signal differences.

Transformation of non-neuritic into neuritic plaques during AD progression drives cortical spread of tau pathology via regenerative failure

Extracellular amyloid-β (Aβ) plaques and intracellular aggregates of tau protein in form of neurofibrillary tangles (NFT) are pathological hallmarks of Alzheimer's disease (AD). The exact mechanism how these two protein aggregates interact in AD is still a matter of debate. Neuritic plaques (NP), a subset of Aβ plaques containing dystrophic neurites (DN), are suggested to be unique to AD and might play a role in the interaction of Aβ and tau. Quantifying NP and non-NP in postmortem brain specimens from patients with increasing severity of AD neuropathological changes (ADNC), we demonstrate that the total number of Aβ plaques and NP increase, while the number of non-NP stagnates. Furthermore, investigating the correlation between NP and NFT, we identified unexpected brain region-specific differences when comparing cases with increasingly more severe ADNC. In neocortical regions NFT counts increase in parallel with NP counts during the progression of ADNC, while this correlation is not observed in hippocampus. These data support the notion that non-NP are transformed into NP during the progression of ADNC and indicate that NP might drive cortical NFT formation. Next, using spatial transcriptomics, we analyzed the gene expression profile of the microenvironment around non-NP and NP. We identified an upregulation of neuronal systems and Ca-dependent event pathways around NP compared to non-NP. We speculate that the upregulation of these transcripts may hint at a compensatory mechanism underlying NP formation. Our studies suggest that the transformation of non-NP to NP is a key event in ADNC progression and points to regenerative failure as a potential driving force of this process.

The role of LSM1 in breast cancer: Shaping metabolism and tumor-associated macrophage infiltration

LSM1 is part of the cytoplasmic protein complex Lsm1-7-Pat1 and is likely involved in pre-mRNA degradation by aiding U4/U6 snRNP formation. More research is needed to uncover LSM1's potential in breast cancer (BRCA) clinical pathology, the tumor immune microenvironment, and precision oncology. We discovered LSM1 as a diagnostic marker for advanced BRCA with poor survival, using a multi-omics approach. We studied LSM1 expression across BRCA regions and its link to immune cells through various methods, including spatial transcriptomics and single-cell RNA-sequencing. We also examined how silencing LSM1 affects mitochondrial function and energy metabolism in the tumor environment. These findings were confirmed using 54 BRCA patient biopsies and tissue microarrays. Immunofluorescence and bioinformatics assessed LSM1's connection to clinicopathological features and prognosis. This study uncovers gene patterns linked to breast cancer, with LSM1 linked to macrophage energy processes. Silencing LSM1 in breast cancer cells disrupts mitochondria and energy metabolism. Spatial analysis aligns with previous results, showing LSM1's connection to macrophages. Biopsies confirm LSM1 elevation in advanced breast cancer with increased macrophage presence. To summarize, LSM1 changes may drive BRCA progression, making it a potential diagnostic and prognostic marker. It also influences energy metabolism and the tumor's immune environment during metastasis, showing promise for precision medicine and drug screening in BRCA.

Talniflumate abrogates mucin immune suppressive barrier improving efficacy of gemcitabine and nab-paclitaxel treatment in pancreatic cancer

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease. This is due to its aggressive course, late diagnosis and its intrinsic drugs resistance. The complexity of the tumor, in terms of cell components and heterogeneity, has led to the approval of few therapies with limited efficacy. The study of the early stages of carcinogenesis provides the opportunity for the identification of actionable pathways that underpin therapeutic resistance.

METHODS

We analyzed 43 Intraductal papillary mucinous neoplasms (IPMN) (12 Low-grade and 31 High-grade) by Spatial Transcriptomics. Mouse and human pancreatic cancer organoids and T cells interaction platforms were established to test the role of mucins expression on T cells activity. Syngeneic mouse model of PDAC was used to explore the impact of mucins downregulation on standard therapy efficacy.

RESULTS

Spatial transcriptomics showed that mucin O-glycosylation pathway is increased in the progression from low-grade to high-grade IPMN. We identified GCNT3, a master regulator of mucins expression, as an actionable target of this pathway by talniflumate. We showed that talniflumate impaired mucins expression increasing T cell activation and recognition using both mouse and human organoid interaction platforms. In vivo experiments showed that talniflumate was able to increase the efficacy of the chemotherapy by boosting immune infiltration.

CONCLUSIONS

Finally, we demonstrated that combination of talniflumate, an anti-inflammatory drug, with chemotherapy effectively improves anti-tumor effect in PDAC.

A SELECTIVE REVIEW OF RECENT DEVELOPMENTS IN SPATIALLY VARIABLE GENE DETECTION FOR SPATIAL TRANSCRIPTOMICS

With the emergence of advanced spatial transcriptomic technologies, there has been a surge in research papers dedicated to analyzing spatial transcriptomics data, resulting in significant contributions to our understanding of biology. The initial stage of downstream analysis of spatial transcriptomic data has centered on identifying spatially variable genes (SVGs) or genes expressed with specific spatial patterns across the tissue. SVG detection is an important task since many downstream analyses depend on these selected SVGs. Over the past few years, a plethora of new methods have been proposed for the detection of SVGs, accompanied by numerous innovative concepts and discussions. This article provides a selective review of methods and their practical implementations, offering valuable insights into the current literature in this field.

Spatial transcriptomics and neurofilament light chain reveal changes in lesion patterns in murine autoimmune neuroinflammation

OBJECTIVE

Ongoing neuroaxonal damage is a major contributor to disease progression and long-term disability in multiple sclerosis. However, spatio-temporal distribution and pathophysiological mechanisms of neuroaxonal damage during acute relapses and later chronic disease stages remain poorly understood.

METHODS

Here, we applied immunohistochemistry, single-molecule array, spatial transcriptomics, and microglia/axon co-cultures to gain insight into spatio-temporal neuroaxonal damage in experimental autoimmune encephalomyelitis (EAE).

RESULTS

Association of spinal cord white matter lesions and blood-based neurofilament light (sNfL) levels revealed a distinct, stage-dependent anatomical pattern of neuroaxonal damage: in chronic EAE, sNfL levels were predominately associated with anterolateral lumbar lesions, whereas in early EAE sNfL showed no correlation with lesions in any anatomical location. Furthermore, neuroaxonal damage in late EAE was largely confined to white matter lesions but showed a widespread distribution in early EAE. Following this pattern of neuroaxonal damage, spatial transcriptomics revealed a widespread cyto- and chemokine response at early disease stages, whereas late EAE was characterized by a prominent glial cell accumulation in white matter lesions. These findings were corroborated by immunohistochemistry and microglia/axon co-cultures, which further revealed a strong association between CNS myeloid cell activation and neuroaxonal damage both in vivo and in vitro.

INTERPRETATION

Our findings indicate that CNS myeloid cells may play a crucial role in driving neuroaxonal damage in EAE. Moreover, neuroaxonal damage can progress in a stage-dependent centripetal manner, transitioning from normal-appearing white matter to focal white matter lesions. These insights may contribute to a better understanding of neurodegeneration and elevated sNfL levels observed in multiple sclerosis patients at different disease stages.

Unraveling temporal and spatial biomarkers of epithelial-mesenchymal transition in colorectal cancer: insights into the crucial role of immunosuppressive cells

The occurrence and progression of tumors can be established through a complex interplay among tumor cells undergoing epithelial-mesenchymal transition (EMT), invasive factors and immune cells. In this study, we employed single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (ST) to evaluate the pseudotime trajectory and spatial interactive relationship between EMT-invasive malignant tumors and immune cells in primary colorectal cancer (CRC) tissues at different stages (stage I/II and stage III with tumor deposit). Our research characterized the spatiotemporal relationship among different invasive tumor programs by constructing pseudotime endpoint-EMT-invasion tumor programs (EMTPs) located at the edge of ST, utilizing evolution trajectory analysis integrated with EMT-invasion genes. Strikingly, the invasive and expansive process of tumors undergoes remarkable spatial reprogramming of regulatory and immunosuppressive cells, such as myeloid-derived suppressor cells (MDSCs), tumor-associated macrophages (TAMs), regulatory T cells (Treg), and exhausted T cells (Tex). These EMTP-adjacent cell are linked to EMT-related invasion genes, especially the C-X-C motif ligand 1 (CXCL1) and CXCL8 genes that are important for CRC prognosis. Interestingly, the EMTPs in stage I mainly produce an inflammatory margin invasive niche, while the EMTPs in stage III tissues likely produce a hypoxic pre-invasive niche. Our data demonstrate the crucial role of regulatory and immunosuppressive cells in tumor formation and progression of CRC. This study provides a framework to delineate the spatiotemporal invasive niche in CRC samples.