BACKGROUND: The combination of Sorafenib and transcatheter arterial chemoembolization (TACE) exhibits limited efficacy in the treatment of certain advanced hepatocellular carcinomas (HCC), and the molecular mechanisms underlying resistance to this combination remain unclear.
OBJECTIVE: This study aims to underscore the distinctive contribution of GeoMx DSP technology in elucidating the molecular intricacies of HCC resistance to the Sorafenib and TACE combination.
METHODS: Patients with advanced HCC during the waiting period before liver transplantation were classified into sensitive and resistant groups based on their response to Sorafenib and TACE combination therapy. Employing GeoMx DSP technology for comprehensive gene expression profiling, we identified pivotal molecular targets linked to resistance against combination therapy.
RESULTS: The investigation scrutinized intra-tumoral and inter-individual variances, unveiling a spectrum of crucial molecular targets, such as PLG, PLVAP, immunoglobulin genes, ORM1, and NR4A1, among others. Additionally, we explored signaling pathways associated with treatment responsiveness, including the PPAR signaling pathway. Notably, we emphasized the significance of the immune microenvironment characterized by heightened SPP1 expression in HCC resistance to combination therapy. In the resistant group, SPP1+ tumor-associated macrophage (TAM) infiltration was notably pronounced (p = 0.037), while T-cell depletion showed a mitigated presence (p = 0.013).
CONCLUSIONS: The study reveals intra- and inter-individual heterogeneity in HCC that is differentially responsive to the combination of Sorafenib and TACE, highlighting multiple key molecular targets associated with treatment resistance. The immune microenvironment is important, and in particular, SPP1+ TAM infiltration may play a key role. Meanwhile, the introduction of immunotherapy in patients resistant to combination therapy may lead to positive results.

Mutations are abundantly present in tissues of healthy individuals, including the breast epithelium. Yet it remains unknown whether mutant cells directly induce lesion formation or first spread, leading to a field of mutant cells that is predisposed towards lesion formation. To study the clonal and spatial relationships between morphologically normal breast epithelium adjacent to pre-cancerous lesions, we developed a three-dimensional (3D) imaging pipeline combined with spatially resolved genomics on archival, formalin-fixed breast tissue with the non-obligate breast cancer precursor ductal carcinoma in situ (DCIS). Using this 3D image-guided characterization method, we built high-resolution spatial maps of DNA copy number aberration (CNA) profiles within the DCIS lesion and the surrounding normal mammary ducts. We show that the local heterogeneity within a DCIS lesion is limited. However, by mapping the CNA profiles back onto the 3D reconstructed ductal subtree, we find that in eight out of 16 cases the healthy epithelium adjacent to the DCIS lesions has overlapping structural variations with the CNA profile of the DCIS. Together, our study indicates that pre-malignant breast transformations frequently develop within mutant clonal fields of morphologically normal-looking ducts. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Cancer cells can manipulate immune cells and escape from the immune system response. Quantifying the molecular changes that occur when an immune cell touches a tumor cell can increase our understanding of the underlying mechanisms. Recently, it became possible to perform such measurements in situ-for example, using expansion sequencing, which enabled in situ sequencing of genes with super-resolution. We systematically examined whether individual immune cells from specific cell types express genes differently when in physical proximity to individual tumor cells. First, we demonstrated that a dense mapping of genes in situ can be used for the segmentation of cell bodies in 3D, thus improving our ability to detect likely touching cells. Next, we used three different computational approaches to detect the molecular changes that are triggered by proximity: differential expression analysis, tree-based machine learning classifiers, and matrix factorization analysis. This systematic analysis revealed tens of genes, in specific cell types, whose expression separates immune cells that are proximal to tumor cells from those that are not proximal, with a significant overlap between the different detection methods. Remarkably, an order of magnitude more genes are triggered by proximity to tumor cells in CD8 T cells compared to CD4 T cells, in line with the ability of CD8 T cells to directly bind major histocompatibility complex (MHC) class I on tumor cells. Thus, in situ sequencing of an individual biopsy can be used to detect genes likely involved in immune-tumor cell-cell interactions. The data used in this manuscript and the code of the InSituSeg, machine learning, cNMF, and Moran\'s I methods are publicly available at doi:10.5281/zenodo.7497981.

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.

Genome-wide ensemble sequencing methods improved our understanding of chromatin organization in eukaryotes but lack the ability to capture single-cell heterogeneity and spatial organization. To overcome these limitations, new imaging-based methods have emerged, giving rise to the field of spatial genomics. Here, we present pyHiM, a user-friendly python toolbox specifically designed for the analysis of multiplexed DNA-FISH data and the reconstruction of chromatin traces in individual cells. pyHiM employs a modular architecture, allowing independent execution of analysis steps and customization according to sample specificity and computing resources. pyHiM aims to facilitate the democratization and standardization of spatial genomics analysis.

With the development of the social economy and the deepening understanding of cancer, cancer has become a significant cause of death, threatening human health. Although researchers have made rapid progress in cancer treatment strategies in recent years, the overall survival of cancer patients is still not optimistic. Therefore, it is essential to reveal the spatial pattern of gene expression, spatial heterogeneity of cell populations, microenvironment interactions, and other aspects of cancer. Spatiotemporal transcriptomics can help analyze the mechanism of cancer occurrence and development, greatly help precise cancer treatment, and improve clinical prognosis. Here, we review the integration strategies of single-cell RNA sequencing and spatial transcriptomics data, summarize the recent advances in spatiotemporal transcriptomics in cancer studies, and discuss the combined application of spatial multiomics, which provides new directions and strategies for the precise treatment and clinical prognosis of cancer.

Eukaryotic genomes are spatially organized inside the cell nucleus, forming a threedimensional (3D) architecture that allows for spatial separation of nuclear processes and for controlled expression of genes required for cell identity specification and tissue homeostasis. Hence, it is of no surprise that mis-regulation of genome architecture through rearrangements of the linear genome sequence or epigenetic perturbations are often linked to aberrant gene expression programs in tumor cells. Increasing research efforts have shed light into the causes and consequences of alterations of 3D genome organization. In this review, we summarize the current knowledge on how 3D genome architecture is dysregulated in cancer, with a focus on enhancer highjacking events and their contribution to tumorigenesis. Studying the functional effects of genome architecture perturbations on gene expression in cancer offers a unique opportunity for a deeper understanding of tumor biology and sets the basis for the discovery of novel therapeutic targets.

The mammalian cerebral cortex shows functional specialization into regions with distinct neuronal compositions, most strikingly in the human brain, but little is known in about how cellular lineages shape cortical regional variation and neuronal cell types during development. Here, we use somatic single nucleotide variants (sSNVs) to map lineages of neuronal sub-types and cortical regions. Early-occurring sSNVs rarely respect Brodmann area (BA) borders, while late-occurring sSNVs mark neuron-generating clones with modest regional restriction, though descendants often dispersed into neighboring BAs. Nevertheless, in visual cortex, BA17 contains 30-70% more sSNVs compared to the neighboring BA18, with clones across the BA17/18 border distributed asymmetrically and thus displaying different cortex-wide dispersion patterns. Moreover, we find that excitatory neuron-generating clones with modest regional restriction consistently share low-mosaic sSNVs with some inhibitory neurons, suggesting significant co-generation of excitatory and some inhibitory neurons in the dorsal cortex. Our analysis reveals human-specific cortical cell lineage patterns, with both regional inhomogeneities in progenitor proliferation and late divergence of excitatory/inhibitory lineages.

The aim of this study is to provide a comprehensive overview of spatial multiomics analysis, including its definition, processes, applications, significance and relevant research in psychiatric disorders. To achieve this, a literature search was conducted, focusing on three major spatial omics techniques and their application to three common psychiatric disorders: Alzheimer\'s disease (AD), schizophrenia, and autism spectrum disorders. Spatial genomics analysis has revealed specific genes associated with neuropsychiatric disorders in certain brain regions. Spatial transcriptomics analysis has identified genes related to AD in areas such as the hippocampus, olfactory bulb, and middle temporal gyrus. It has also provided insight into the response to AD in mouse models. Spatial proteogenomics has identified autism spectrum disorder (ASD)-risk genes in specific cell types, while schizophrenia risk loci have been linked to transcriptional signatures in the human hippocampus. In summary, spatial multiomics analysis offers a powerful approach to understand AD pathology and other psychiatric diseases, integrating multiple data modalities to identify risk genes for these disorders. It is valuable for studying psychiatric disorders with high or low cellular heterogeneity and provides new insights into the brain nucleome to predict disease progression and aid diagnosis and treatment.

The devastating effects of Alzheimer\'s disease (AD) are yet to be ameliorated due to the absence of curative treatment options. AD is an aging-related disease that affects cognition, and molecular imbalance is one of its hallmarks. There is a need to identify common causes of molecular imbalance in AD and their potential mechanisms for continuing research. A narrative synthesis of molecular mechanisms in AD from primary studies that employed single-cell sequencing (scRNA-seq) or spatial genomics was conducted using Embase and PubMed databases. We found that differences in molecular mechanisms in AD could be grouped into four key categories: sex-specific features, early-onset features, aging, and immune system pathways. The reported causes of molecular imbalance were alterations in bile acid (BA) synthesis, PITRM1, TREM2, olfactory mucosa (OM) cells, cholesterol catabolism, NFkB, double-strand break (DSB) neuronal damage, P65KD silencing, tau and APOE expression. What changed from previous findings in contrast to results obtained were explored to find potential factors for AD-modifying investigations.