Laser Capture Microdissection and RNA-Seq Analysis: High Sensitivity Approaches to Explain Histopathological Heterogeneity in Human Glioblastoma FFPE Archived Tissues

An optimized workflow for transcriptomic analysis from archival paraformaldehyde-fixed retinal tissues collected by laser capture microdissection

RNA sequencing (RNA-seq) coupled with laser capture microdissection (LCM) is a powerful tool for transcriptomic analysis in unfixed fresh-frozen tissues. Fixation of ocular tissues for immunohistochemistry commonly involves the use of paraformaldehyde (PFA) followed by embedding in Optimal Cutting Temperature (OCT) medium for long-term cryopreservation. However, the quality of RNA derived from such archival PFA-fixed/OCT-embedded samples is often compromised, limiting its suitability for transcriptomic studies. In this study, we aimed to develop a methodology to extract high-quality RNA from PFA-fixed canine eyes by utilizing LCM to isolate retinal tissue. We demonstrate the efficacy of an optimized LCM and RNA purification protocol for transcriptomic profiling of PFA-fixed retinal specimens. We compared four pairs of canine retinal tissues, where one eye was subjected to PFA-fixation prior to OCT embedding, while the contralateral eye was embedded fresh frozen (FF) in OCT without fixation. Since the RNA obtained from PFA-fixed retinas were contaminated with genomic DNA, we employed two rounds of DNase I treatment to obtain RNA suitable for RNA-seq. Notably, the quality of sequencing reads and gene sets identified from both PFA-fixed and FF tissues were nearly identical. In summary, our study introduces an optimized workflow for transcriptomic profiling from PFA-fixed archival retina. This refined methodology paves the way for improved transcriptomic analysis of preserved ocular tissue, bridging the gap between optimal sample preservation and high-quality RNA data acquisition.

Streamlined Full-Length Total RNA Sequencing of Paraformaldehyde-Fixed Brain Tissues

Paraformaldehyde (PFA) fixation is the preferred method for preserving tissue architecture for anatomical and pathological observations. Meanwhile, PFA reacts with the amine groups of biomolecules to form chemical cross-linking, which preserves RNA within the tissue. This has great prospects for RNA sequencing to characterize the molecular underpinnings after anatomical and pathological observations. However, RNA is inaccessible due to cross-linked adducts forming between RNA and other biomolecules in prolonged PFA-fixed tissue. It is also difficult to perform reverse transcription and PCR, resulting in low sequencing sensitivity and reduced reproducibility. Here, we developed a method to perform RNA sequencing in PFA-fixed tissue, which is easy to use, cost-effective, and allows efficient sample multiplexing. We employ cross-link reversal to recover RNA and library construction using random primers without artificial fragmentation. The yield and quality of recovered RNA significantly increased through our method, and sequencing quality metrics and detected genes did not show any major differences compared with matched fresh samples. Moreover, we applied our method for gene expression analysis in different regions of the mouse brain and identified unique gene expression profiles with varied functional implications. We also find significant dysregulation of genes involved in Alzheimer's disease (AD) pathogenesis within the medial septum (MS)/vertical diagonal band of Broca (VDB) of the 5×FAD mouse brain. Our method can thus increase the performance of high-throughput RNA sequencing with PFA-fixed samples and allows longitudinal studies of small tissue regions isolated by their in situ context.

The Role of Mesenchymal Reprogramming in Malignant Clonal Evolution and Intra-Tumoral Heterogeneity in Glioblastoma

Glioblastoma (GBM) is the most common yet uniformly fatal adult brain cancer. Intra-tumoral molecular and cellular heterogeneities are major contributory factors to therapeutic refractoriness and futility in GBM. Molecular heterogeneity is represented through molecular subtype clusters whereby the proneural (PN) subtype is associated with significantly increased long-term survival compared to the highly resistant mesenchymal (MES) subtype. Furthermore, it is universally recognized that a small subset of GBM cells known as GBM stem cells (GSCs) serve as reservoirs for tumor recurrence and progression. The clonal evolution of GSC molecular subtypes in response to therapy drives intra-tumoral heterogeneity and remains a critical determinant of GBM outcomes. In particular, the intra-tumoral MES reprogramming of GSCs using current GBM therapies has emerged as a leading hypothesis for therapeutic refractoriness. Preventing the intra-tumoral divergent evolution of GBM toward the MES subtype via new treatments would dramatically improve long-term survival for GBM patients and have a significant impact on GBM outcomes. In this review, we examine the challenges of the role of MES reprogramming in the malignant clonal evolution of glioblastoma and provide future perspectives for addressing the unmet therapeutic need to overcome resistance in GBM.

Raman cell sorting for single-cell research

Cells constitute the fundamental units of living organisms. Investigating individual differences at the single-cell level facilitates an understanding of cell differentiation, development, gene expression, and cellular characteristics, unveiling the underlying laws governing life activities in depth. In recent years, the integration of single-cell manipulation and recognition technologies into detection and sorting systems has emerged as a powerful tool for advancing single-cell research. Raman cell sorting technology has garnered attention owing to its non-labeling, non-destructive detection features and the capability to analyze samples containing water. In addition, this technology can provide live cells for subsequent genomics analysis and gene sequencing. This paper emphasizes the importance of single-cell research, describes the single-cell research methods that currently exist, including single-cell manipulation and single-cell identification techniques, and highlights the advantages of Raman spectroscopy in the field of single-cell analysis by comparing it with the fluorescence-activated cell sorting (FACS) technique. It describes various existing Raman cell sorting techniques and introduces their respective advantages and disadvantages. The above techniques were compared and analyzed, considering a variety of factors. The current bottlenecks include weak single-cell spontaneous Raman signals and the requirement for a prolonged total cell exposure time, significantly constraining Raman cell sorting technology's detection speed, efficiency, and throughput. This paper provides an overview of current methods for enhancing weak spontaneous Raman signals and their associated advantages and disadvantages. Finally, the paper outlines the detailed information related to the Raman cell sorting technology mentioned in this paper and discusses the development trends and direction of Raman cell sorting.

Promise of spatially resolved omics for tumor research

Tumors are spatially heterogeneous tissues that comprise numerous cell types with intricate structures. By interacting with the microenvironment, tumor cells undergo dynamic changes in gene expression and metabolism, resulting in spatiotemporal variations in their capacity for proliferation and metastasis. In recent years, the rapid development of histological techniques has enabled efficient and high-throughput biomolecule analysis. By preserving location information while obtaining a large number of gene and molecular data, spatially resolved metabolomics (SRM) and spatially resolved transcriptomics (SRT) approaches can offer new ideas and reliable tools for the in-depth study of tumors. This review provides a comprehensive introduction and summary of the fundamental principles and research methods used for SRM and SRT techniques, as well as a review of their applications in cancer-related fields.

Dissecting the brain with spatially resolved multi-omics

Recent studies have highlighted spatially resolved multi-omics technologies, including spatial genomics, transcriptomics, proteomics, and metabolomics, as powerful tools to decipher the spatial heterogeneity of the brain. Here, we focus on two major approaches in spatial transcriptomics (next-generation sequencing-based technologies and image-based technologies), and mass spectrometry imaging technologies used in spatial proteomics and spatial metabolomics. Furthermore, we discuss their applications in neuroscience, including building the brain atlas, uncovering gene expression patterns of neurons for special behaviors, deciphering the molecular basis of neuronal communication, and providing a more comprehensive explanation of the molecular mechanisms underlying central nervous system disorders. However, further efforts are still needed toward the integrative application of multi-omics technologies, including the real-time spatial multi-omics analysis in living cells, the detailed gene profile in a whole-brain view, and the combination of functional verification.

Exploring the Vital Link Between Glioma, Neuron, and Neural Activity in the Context of Invasion

Because of their ability to infiltrate normal brain tissue, gliomas frequently evade microscopic surgical excision. The histologic infiltrative property of human glioma has been previously characterized as Scherer secondary structures, of which the perivascular satellitosis is a prospective target for anti-angiogenic treatment in high-grade gliomas. However, the mechanisms underlying perineuronal satellitosis remain unclear, and therapy remains lacking. Our knowledge of the mechanism underlying Scherer secondary structures has improved over time. New techniques, such as laser capture microdissection and optogenetic stimulation, have advanced our understanding of glioma invasion mechanisms. Although laser capture microdissection is a useful tool for studying gliomas that infiltrate the normal brain microenvironment, optogenetics and mouse xenograft glioma models have been extensively used in studies demonstrating the unique role of synaptogenesis in glioma proliferation and identification of potential therapeutic targets. Moreover, a rare glioma cell line is established that, when transplanted in the mouse brain, can replicate and recapitulate the human diffuse invasion phenotype. This review discusses the primary molecular causes of glioma, its histopathology-based invasive mechanisms, and the importance of neuronal activity and interactions between glioma cells and neurons in the brain microenvironment. It also explores current methods and models of gliomas.

Application of spatial transcriptome technologies to neurological diseases

Spatial transcriptome technology acquires gene expression profiles while retaining spatial location information, it displays the gene expression properties of cells in situ. Through the investigation of cell heterogeneity, microenvironment, function, and cellular interactions, spatial transcriptome technology can deeply explore the pathogenic mechanisms of cell-type-specific responses and spatial localization in neurological diseases. The present article overviews spatial transcriptome technologies based on microdissection, in situ hybridization, in situ sequencing, in situ capture, and live cell labeling. Each technology is described along with its methods, detection throughput, spatial resolution, benefits, and drawbacks. Furthermore, their applications in neurodegenerative disease, neuropsychiatric illness, stroke and epilepsy are outlined. This information can be used to understand disease mechanisms, pick therapeutic targets, and establish biomarkers.

Light-Seq: light-directed in situ barcoding of biomolecules in fixed cells and tissues for spatially indexed sequencing

We present Light-Seq, an approach for multiplexed spatial indexing of intact biological samples using light-directed DNA barcoding in fixed cells and tissues followed by ex situ sequencing. Light-Seq combines spatially targeted, rapid photocrosslinking of DNA barcodes onto complementary DNAs in situ with a one-step DNA stitching reaction to create pooled, spatially indexed sequencing libraries. This light-directed barcoding enables in situ selection of multiple cell populations in intact fixed tissue samples for full-transcriptome sequencing based on location, morphology or protein stains, without cellular dissociation. Applying Light-Seq to mouse retinal sections, we recovered thousands of differentially enriched transcripts from three cellular layers and discovered biomarkers for a very rare neuronal subtype, dopaminergic amacrine cells, from only four to eight individual cells per section. Light-Seq provides an accessible workflow to combine in situ imaging and protein staining with next generation sequencing of the same cells, leaving the sample intact for further analysis post-sequencing.

Light-Seq uses light-directed DNA barcoding in fixed cells and tissues for multiplexed spatial indexing and subsequent next generation sequencing. This approach blends spatial and omics information to enable analysis of rare cell types in complex tissues.

Claudin-1 mediates progression by regulating EMT through AMPK/TGF-β signaling in head and neck squamous cell carcinoma

Claudin-1 (CLDN1), a major component of tight junction complexes in the epithelium, maintains cellular polarity, and plays a critical role in cell-to-cell communication as well as epithelial cell homeostasis. Although the role of CLDN1 has been widely studied in cancer, its role in the progression and the exact regulatory mechanisms, remain controversial. Using next-generation sequencing, we first analyzed the expression profiles of tumor/non-tumor paired tissue in patients with head and neck squamous cell carcinoma (HNSC) from public and local cohorts and found out that CLDN1 is upregulated in tumors compared to normal tissues. Next, its correlation with lymph node metastasis and poor prognosis was validated in the retrospective cohort, which collectively suggests CLDN1 as an oncogene in HNSC. As expected, the knockdown of CLDN1 inhibited invasive phenotypes by downregulating epithelial-to-mesenchymal transition (EMT) in vitro. To ascertain the regulatory mechanism of CLDN1 in HNSC analysis of GO term enrichment, KEGG pathways, and curated gene sets were used. As a result, CLDN1 was negatively associated with AMP-activated protein kinase (AMPK) and positively associated with transforming growth factor-β (TGF-β) signaling. In vitro mechanistic assay showed that CLDN1 inhibited AMPK phosphorylation by regulating AMPK upstream phosphatases, which led to inhibition of Smad2 activity. Intriguingly, the invasive phenotype of cancer cells increased by CLDN1 overexpression was rescued by AMPK activation, indicating a role of the CLDN1/AMPK/TGF-β/EMT cascade in HNSC. Consistently in vivo, CLDN1 suppression significantly inhibited the tumor growth, with elevated AMPK expression, suggesting the novel observation of oncogenic CLDN1-AMPK signaling in HNSC.

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.

Single-Cell RNA Sequencing with Spatial Transcriptomics of Cancer Tissues

Single-cell RNA sequencing (RNA-seq) techniques can perform analysis of transcriptome at the single-cell level and possess an unprecedented potential for exploring signatures involved in tumor development and progression. These techniques can perform sequence analysis of transcripts with a better resolution that could increase understanding of the cellular diversity found in the tumor microenvironment and how the cells interact with each other in complex heterogeneous cancerous tissues. Identifying the changes occurring in the genome and transcriptome in the spatial context is considered to increase knowledge of molecular factors fueling cancers. It may help develop better monitoring strategies and innovative approaches for cancer treatment. Recently, there has been a growing trend in the integration of RNA-seq techniques with contemporary omics technologies to study the tumor microenvironment. There has been a realization that this area of research has a huge scope of application in translational research. This review article presents an overview of various types of single-cell RNA-seq techniques used currently for analysis of cancer tissues, their pros and cons in bulk profiling of transcriptome, and recent advances in the techniques in exploring heterogeneity of various types of cancer tissues. Furthermore, we have highlighted the integration of single-cell RNA-seq techniques with other omics technologies for analysis of transcriptome in their spatial context, which is considered to revolutionize the understanding of tumor microenvironment.

RPP25 as a Prognostic-Related Biomarker That Correlates With Tumor Metabolism in Glioblastoma

RPP25, a 25 kDa protein subunit of ribonuclease P (RNase P), is a protein-coding gene. Disorders associated with RPP25 include chromosome 15Q24 deletion syndrome and diffuse scleroderma, while systemic sclerosis can be complicated by malignancy. However, the functional role of RPP25 expression in glioblastoma multiforme (GBM) is unclear. In this study, comprehensive bioinformatics analysis was used to evaluate the impact of RPP25 on GBM occurrence and prognosis. Differential analysis of multiple databases showed that RPP25 was commonly highly expressed in multiple cancers but lowly expressed in GBM. Survival prognostic results showed that RPP25 was prognostically relevant in six tumors (CESC, GBM, LAML, LUAD, SKCM, and UVM), but high RPP25 expression was significantly associated with poor patient prognosis except for CESC. Analysis of RPP25 expression in GBM alone revealed that RPP25 was significantly downregulated in GBM compared with normal tissue. Receiver operating characteristic (ROC) combined with Kaplan-Meier (KM) analysis and Cox regression analysis showed that high RPP25 expression was a prognostic risk factor for GBM and had a predictive value for the 1-year, 2-year, and 3-year survival of GBM patients. In addition, the expression of RPP25 was correlated with the level of immune cell infiltration. The gene set enrichment analysis (GSEA) results showed that RPP25 was mainly associated with signalling pathways related to tumor progression and tumor metabolism.

Wholistic approach - transcriptomic analysis and beyond using archival material for molecular diagnosis

Many neoplasms remain unclassified after histopathological examination, which requires further molecular analysis. To this regard, mesenchymal neoplasms are particularly challenging due to the combination of their rarity and the large number of subtypes, and many entities still lack robust diagnostic hallmarks. RNA transcriptomic profiles have proven to be a reliable basis for the classification of previously unclassified tumors and notably for mesenchymal neoplasms. Using exome-based RNA capture sequencing on more than 5000 samples of archival material (FFPE), the combination of expression profiles analyzes (including several clustering methods), fusion genes, and small nucleotide variations has been developed at the Centre Léon Bérard (CLB) in Lyon for the molecular diagnosis of challenging neoplasms and the discovery of new entities. The molecular basis of the technique, the protocol, and the bioinformatics algorithms used are described herein, as well as its advantages and limitations.

Topographic mapping of the glioblastoma proteome reveals a triple-axis model of intra-tumoral heterogeneity

Glioblastoma is an aggressive form of brain cancer with well-established patterns of intra-tumoral heterogeneity implicated in treatment resistance and progression. While regional and single cell transcriptomic variations of glioblastoma have been recently resolved, downstream phenotype-level proteomic programs have yet to be assigned across glioblastoma's hallmark histomorphologic niches. Here, we leverage mass spectrometry to spatially align abundance levels of 4,794 proteins to distinct histologic patterns across 20 patients and propose diverse molecular programs operational within these regional tumor compartments. Using machine learning, we overlay concordant transcriptional information, and define two distinct proteogenomic programs, MYC- and KRAS-axis hereon, that cooperate with hypoxia to produce a tri-dimensional model of intra-tumoral heterogeneity. Moreover, we highlight differential drug sensitivities and relative chemoresistance in glioblastoma cell lines with enhanced KRAS programs. Importantly, these pharmacological differences are less pronounced in transcriptional glioblastoma subgroups suggesting that this model may provide insights for targeting heterogeneity and overcoming therapy resistance.

Spatial omics: Navigating to the golden era of cancer research

The idea that tumour microenvironment (TME) is organised in a spatial manner will not surprise many cancer biologists; however, systematically capturing spatial architecture of TME is still not possible until recent decade. The past five years have witnessed a boom in the research of high-throughput spatial techniques and algorithms to delineate TME at an unprecedented level. Here, we review the technological progress of spatial omics and how advanced computation methods boost multi-modal spatial data analysis. Then, we discussed the potential clinical translations of spatial omics research in precision oncology, and proposed a transfer of spatial ecological principles to cancer biology in spatial data interpretation. So far, spatial omics is placing us in the golden age of spatial cancer research. Further development and application of spatial omics may lead to a comprehensive decoding of the TME ecosystem and bring the current spatiotemporal molecular medical research into an entirely new paradigm.

Genome-wide spatial expression profiling in formalin-fixed tissues

Formalin-fixed paraffin embedding (FFPE) is the most widespread long-term tissue preservation approach. Here, we report a procedure to perform genome-wide spatial analysis of mRNA in FFPE-fixed tissue sections, using well-established, commercially available methods for imaging and spatial barcoding using slides spotted with barcoded oligo(dT) probes to capture the 3' end of mRNA molecules in tissue sections. We applied this method for expression profiling and cell type mapping in coronal sections from the mouse brain to demonstrate the method's capability to delineate anatomical regions from a molecular perspective. We also profiled the spatial composition of transcriptomic signatures in two ovarian carcinosarcoma samples, exemplifying the method's potential to elucidate molecular mechanisms in heterogeneous clinical samples. Finally, we demonstrate the applicability of the assay to characterize human lung and kidney organoids and a human lung biopsy specimen infected with SARS-CoV-2. We anticipate that genome-wide spatial gene expression profiling in FFPE biospecimens will be used for retrospective analysis of biobank samples, which will facilitate longitudinal studies of biological processes and biomarker discovery.

Multi-tumor analysis of cancer-stroma interactomes of patient-derived xenografts unveils the unique homeostatic process in renal cell carcinomas

The patient-derived xenograft (PDX) model is a versatile tool used to study the tumor microenvironment (TME). However, limited studies have described multi-tumor PDX screening strategies to detect hub regulators during cancer-stroma interaction. Transcriptomes of cancer (human) and stroma (mouse) components of 70 PDX samples comprising 9 distinctive tumor types were analyzed in this study. PDX models recapitulated the original tumors' features, including tumor composition and putative signaling. Particularly, kidney renal clear cell carcinoma (KIRC) stood out, with altered hypoxia-related pathways and a high proportion of endothelial cells in the TME. Furthermore, an integrated analysis conducted to predict paracrine effectors in the KIRC cancer-to-stroma communication detected well-established soluble factors responsible for the hypoxia-related reaction and the so-far unestablished soluble factor, apelin (APLN). Subsequent experiments also supported the potential role of APLN in KIRC tumor progression. Therefore, this paper hereby provides an analytical workflow to find hub regulators in cancer-stroma interactions.

Slow Off-Rate Modified Aptamer (SOMAmer) Proteomic Analysis of Patient-Derived Malignant Glioma Identifies Distinct Cellular Proteomes

Malignant gliomas derive from brain glial cells and represent >75% of primary brain tumors. This includes anaplastic astrocytoma (grade III; AS), the most common and fatal glioblastoma multiforme (grade IV; GBM), and oligodendroglioma (ODG). We have generated patient-derived AS, GBM, and ODG cell models to study disease mechanisms and test patient-centered therapeutic strategies. We have used an aptamer-based high-throughput SOMAscan^® 1.3K assay to determine the proteomic profiles of 1307 different analytes. SOMAscan^® proteomes of AS and GBM self-organized into closely adjacent proteomes which were clearly distinct from ODG proteomes. GBM self-organized into four proteomic clusters of which SOMAscan^® cluster 4 proteome predicted a highly inter-connected proteomic network. Several up- and down-regulated proteins relevant to glioma were successfully validated in GBM cell isolates across different SOMAscan^® clusters and in corresponding GBM tissues. Slow off-rate modified aptamer proteomics is an attractive analytical tool for rapid proteomic stratification of different malignant gliomas and identified cluster-specific SOMAscan^® signatures and functionalities in patient GBM cells.

Multiregional Sequencing of IDH-WT Glioblastoma Reveals High Genetic Heterogeneity and a Dynamic Evolutionary History

Simple Summary

Glioblastoma is the most common and aggressive primary brain malignancy in adults. In addition to extensive inter-patient heterogeneity, glioblastoma shows intra-tumor extensive cellular and molecular heterogeneity, both spatially and temporally. This heterogeneity is one of the main reasons for the poor prognosis and overall survival. Moreover, it raises the important question of whether the molecular characterization of a single biopsy sample, as performed in standard diagnostics, actually represents the entire lesion. In this study, we sequenced the whole exome of nine spatially different cancer regions of three primary glioblastomas. We characterized their mutational profiles and copy number alterations, with implications for our understanding of tumor biology in relation to clonal architecture and evolutionary dynamics, as well as therapeutically relevant alterations.

Abstract

Glioblastoma is one of the most common and lethal primary neoplasms of the brain. Patient survival has not improved significantly over the past three decades and the patient median survival is just over one year. Tumor heterogeneity is thought to be a major determinant of therapeutic failure and a major reason for poor overall survival. This work aims to comprehensively define intra- and inter-tumor heterogeneity by mapping the genomic and mutational landscape of multiple areas of three primary IDH wild-type (IDH-WT) glioblastomas. Using whole exome sequencing, we explored how copy number variation, chromosomal and single loci amplifications/deletions, and mutational burden are spatially distributed across nine different tumor regions. The results show that all tumors exhibit a different signature despite the same diagnosis. Above all, a high inter-tumor heterogeneity emerges. The evolutionary dynamics of all identified mutations within each region underline the questionable value of a single biopsy and thus the therapeutic approach for the patient. Multiregional collection and subsequent sequencing are essential to try to address the clinical challenge of precision medicine. Especially in glioblastoma, this approach could provide powerful support to pathologists and oncologists in evaluating the diagnosis and defining the best treatment option.

Disseminated Cryptococcosis Presenting as Cellulitis Diagnosed by Laser Capture Microdissection: A Case Report and Literature Review

Disseminated cryptococcosis primarily affects immunosuppressed patients and has a poor outcome if diagnosis and treatment are delayed. Skin lesions are rarely manifest causing misdiagnosis. We present a case of cryptococcal cellulitis with severe pain in a kidney transplant recipient on long-term immunosuppressive therapy. Multiple organs were involved, and there was cutaneous dissemination of the lesions. Histopathology revealed abundant yeast-like cells with wide capsular halos in subcutaneous tissue, suggesting Cryptococcus spp. infection. Laser capture microdissection (LCM)-PCR on skin biopsies confirmed Cryptococcus neoformans var. grubii. A literature review of 17 cases of disseminated cryptococcosis with cutaneous cellulitis or panniculitis in HIV-negative individuals found that over half the patients (52.9%, 9/17) had a history of glucocorticoid therapy, and that the most common site was the legs (76.5%, 13/17). C. neoformans was the main pathogenic species, accounting for 88.2% (15/17) of cases. Fungal cellulitis should be included in the differential diagnosis of cellulitis that fails to respond to antimicrobial therapy in HIV-negative immunosuppressed individuals. Non-culture-based molecular techniques aid in rapid pathogen identification in histologically positive, unculturable specimens.

Next Generation Imaging Techniques to Define Immune Topographies in Solid Tumors

In recent years, cancer immunotherapy experienced remarkable developments and it is nowadays considered a promising therapeutic frontier against many types of cancer, especially hematological malignancies. However, in most types of solid tumors, immunotherapy efficacy is modest, partly because of the limited accessibility of lymphocytes to the tumor core. This immune exclusion is mediated by a variety of physical, functional and dynamic barriers, which play a role in shaping the immune infiltrate in the tumor microenvironment. At present there is no unified and integrated understanding about the role played by different postulated models of immune exclusion in human solid tumors. Systematically mapping immune landscapes or "topographies" in cancers of different histology is of pivotal importance to characterize spatial and temporal distribution of lymphocytes in the tumor microenvironment, providing insights into mechanisms of immune exclusion. Spatially mapping immune cells also provides quantitative information, which could be informative in clinical settings, for example for the discovery of new biomarkers that could guide the design of patient-specific immunotherapies. In this review, we aim to summarize current standard and next generation approaches to define Cancer Immune Topographies based on published studies and propose future perspectives.

Toward uncharted territory of cellular heterogeneity: advances and applications of single-cell RNA-seq

Among single-cell analysis technologies, single-cell RNA-seq (scRNA-seq) has been one of the front runners in technical inventions. Since its induction, scRNA-seq has been well received and undergone many fast-paced technical improvements in cDNA synthesis and amplification, processing and alignment of next generation sequencing reads, differentially expressed gene calling, cell clustering, subpopulation identification, and developmental trajectory prediction. scRNA-seq has been exponentially applied to study global transcriptional profiles in all cell types in humans and animal models, healthy or with diseases, including cancer. Accumulative novel subtypes and rare subpopulations have been discovered as potential underlying mechanisms of stochasticity, differentiation, proliferation, tumorigenesis, and aging. scRNA-seq has gradually revealed the uncharted territory of cellular heterogeneity in transcriptomes and developed novel therapeutic approaches for biomedical applications. This review of the advancement of scRNA-seq methods provides an exploratory guide of the quickly evolving technical landscape and insights of focused features and strengths in each prominent area of progress.

Satellitosis, a Crosstalk between Neurons, Vascular Structures and Neoplastic Cells in Brain Tumours; Early Manifestation of Invasive Behaviour

The secondary structures of Scherer commonly known as perineuronal and perivascular satellitosis have been identified as a histopathological hallmark of diffuse, invasive, high-grade gliomas. They are recognised as perineuronal satellitosis when clusters of neoplastic glial cells surround neurons cell bodies and perivascular satellitosis when such tumour cells surround blood vessels infiltrating Virchow-Robin spaces. In this review, we provide an overview of emerging knowledge regarding how interactions between neurons and glioma cells can modulate tumour evolution and how neurons play a key role in glioma growth and progression, as well as the role of perivascular satellitosis into mechanisms of glioma cells spread. At the same time, we review the current knowledge about the role of perineuronal satellitosis and perivascular satellitosis within the tumour microenvironment (TME), in order to highlight critical knowledge gaps in research space.

Identification of a Metabolism-Related Risk Signature Associated With Clinical Prognosis in Glioblastoma Using Integrated Bioinformatic Analysis

Altered metabolism of glucose, lipid and glutamine is a prominent hallmark of cancer cells. Currently, cell heterogeneity is believed to be the main cause of poor prognosis of glioblastoma (GBM) and is closely related to relapse caused by therapy resistance. However, the comprehensive model of genes related to glucose-, lipid- and glutamine-metabolism associated with the prognosis of GBM remains unclear, and the metabolic heterogeneity of GBM still needs to be further explored. Based on the expression profiles of 1,395 metabolism-related genes in three datasets of TCGA/CGGA/GSE, consistent cluster analysis revealed that GBM had three different metabolic status and prognostic clusters. Combining univariate Cox regression analysis and LASSO-penalized Cox regression machine learning methods, we identified a 17-metabolism-related genes risk signature associated with GBM prognosis. Kaplan-Meier analysis found that obtained signature could differentiate the prognosis of high- and low-risk patients in three datasets. Moreover, the multivariate Cox regression analysis and receiver operating characteristic curves indicated that the signature was an independent prognostic factor for GBM and had a strong predictive power. The above results were further validated in the CGGA and GSE13041 datasets, and consistent results were obtained. Gene set enrichment analysis (GSEA) suggested glycolysis gluconeogenesis and oxidative phosphorylation were significantly enriched in high- and low-risk GBM. Lastly Connectivity Map screened 54 potential compounds specific to different subgroups of GBM patients. Our study identified a novel metabolism-related gene signature, in addition the existence of three different metabolic status and two opposite biological processes in GBM were recognized, which revealed the metabolic heterogeneity of GBM. Robust metabolic subtypes and powerful risk prognostic models contributed a new perspective to the metabolic exploration of GBM.

Biomimicking Fiber Platform with Tunable Stiffness to Study Mechanotransduction Reveals Stiffness Enhances Oligodendrocyte Differentiation but Impedes Myelination through YAP-Dependent Regulation

A key hallmark of many diseases, especially those in the central nervous system (CNS), is the change in tissue stiffness due to inflammation and scarring. However, how such changes in microenvironment affect the regenerative process remains poorly understood. Here, a biomimicking fiber platform that provides independent variation of fiber structural and intrinsic stiffness is reported. To demonstrate the functionality of these constructs as a mechanotransduction study platform, these substrates are utilized as artificial axons and the effects of axon structural versus intrinsic stiffness on CNS myelination are independently analyzed. While studies have shown that substrate stiffness affects oligodendrocyte differentiation, the effects of mechanical stiffness on the final functional state of oligodendrocyte (i.e., myelination) has not been shown prior to this. Here, it is demonstrated that a stiff mechanical microenvironment impedes oligodendrocyte myelination, independently and distinctively from oligodendrocyte differentiation. Yes-associated protein is identified to be involved in influencing oligodendrocyte myelination through mechanotransduction. The opposing effects on oligodendrocyte differentiation and myelination provide important implications for current work screening for promyelinating drugs, since these efforts have focused mainly on promoting oligodendrocyte differentiation. Thus, the platform may have considerable utility as part of a drug discovery program in identifying molecules that promote both differentiation and myelination.

Artificial intelligence (AI) and big data in cancer and precision oncology

Artificial intelligence (AI) and machine learning have significantly influenced many facets of the healthcare sector. Advancement in technology has paved the way for analysis of big datasets in a cost- and time-effective manner. Clinical oncology and research are reaping the benefits of AI. The burden of cancer is a global phenomenon. Efforts to reduce mortality rates requires early diagnosis for effective therapeutic interventions. However, metastatic and recurrent cancers evolve and acquire drug resistance. It is imperative to detect novel biomarkers that induce drug resistance and identify therapeutic targets to enhance treatment regimes. The introduction of the next generation sequencing (NGS) platforms address these demands, has revolutionised the future of precision oncology. NGS offers several clinical applications that are important for risk predictor, early detection of disease, diagnosis by sequencing and medical imaging, accurate prognosis, biomarker identification and identification of therapeutic targets for novel drug discovery. NGS generates large datasets that demand specialised bioinformatics resources to analyse the data that is relevant and clinically significant. Through these applications of AI, cancer diagnostics and prognostic prediction are enhanced with NGS and medical imaging that delivers high resolution images. Regardless of the improvements in technology, AI has some challenges and limitations, and the clinical application of NGS remains to be validated. By continuing to enhance the progression of innovation and technology, the future of AI and precision oncology show great promise.

Uncovering an Organ's Molecular Architecture at Single-Cell Resolution by Spatially Resolved Transcriptomics

Revealing fine-scale cellular heterogeneity among spatial context and the functional and structural foundations of tissue architecture is fundamental within biological research and pharmacology. Unlike traditional approaches involving single molecules or bulk omics, cutting-edge, spatially resolved transcriptomics techniques offer near-single-cell or even subcellular resolution within tissues. Massive information across higher dimensions along with position-coordinating labels can better map the whole 3D transcriptional landscape of tissues. In this review, we focus on developments and strategies in spatially resolved transcriptomics, compare the cell and gene throughput and spatial resolution in detail for existing methods, and highlight the enormous potential in biomedical research.

Changing Technologies of RNA Sequencing and Their Applications in Clinical Oncology

RNA sequencing (RNAseq) is one of the most commonly used techniques in life sciences, and has been widely used in cancer research, drug development, and cancer diagnosis and prognosis. Driven by various biological and technical questions, the techniques of RNAseq have progressed rapidly from bulk RNAseq, laser-captured micro-dissected RNAseq, and single-cell RNAseq to digital spatial RNA profiling, spatial transcriptomics, and direct in situ sequencing. These different technologies have their unique strengths, weaknesses, and suitable applications in the field of clinical oncology. To guide cancer researchers to select the most appropriate RNAseq technique for their biological questions, we will discuss each of these technologies, technical features, and clinical applications in cancer. We will help cancer researchers to understand the key differences of these RNAseq technologies and their optimal applications.

RNA sequencing by direct tagmentation of RNA/DNA hybrids

RNA sequencing is widely used to measure gene expression in biomedical research; therefore, improvements in the simplicity and accuracy of the technology are desirable. All existing RNA sequencing methods rely on the conversion of RNA into double-stranded DNA through reverse transcription followed by second-strand synthesis. The latter step requires additional enzymes and purification, and introduces sequence-dependent bias. Here, we show that Tn5 transposase, which randomly binds and cuts double-stranded DNA, can directly fragment and prime the RNA/DNA heteroduplexes generated by reverse transcription. The primed fragments are then subject to PCR amplification. This provides an approach for simple and accurate RNA characterization and quantification.

Transcriptome profiling by RNA sequencing (RNA-seq) has been widely used to characterize cellular status, but it relies on second-strand complementary DNA (cDNA) synthesis to generate initial material for library preparation. Here we use bacterial transposase Tn5, which has been increasingly used in various high-throughput DNA analyses, to construct RNA-seq libraries without second-strand synthesis. We show that Tn5 transposome can randomly bind RNA/DNA heteroduplexes and add sequencing adapters onto RNA directly after reverse transcription. This method, Sequencing HEteRo RNA-DNA-hYbrid (SHERRY), is versatile and scalable. SHERRY accepts a wide range of starting materials, from bulk RNA to single cells. SHERRY offers a greatly simplified protocol and produces results with higher reproducibility and GC uniformity compared with prevailing RNA-seq methods.

Leite D, Pilkington GJ. Pre-Clinical Drug Testing in 2D and 3D Human In Vitro Models of Glioblastoma Incorporating Non-Neoplastic Astrocytes: Tunneling Nano Tubules and Mitochondrial Transfer Modulates Cell Behavior and Therapeutic Respons

The role of astrocytes in the glioblastoma (GBM) microenvironment is poorly understood; particularly with regard to cell invasion and drug resistance. To assess this role of astrocytes in GBMs we established an all human 2D co-culture model and a 3D hyaluronic acid-gelatin based hydrogel model (HyStem™-HP) with different ratios of GBM cells to astrocytes. A contact co-culture of fluorescently labelled GBM cells and astrocytes showed that the latter promotes tumour growth and migration of GBM cells. Notably, the presence of non-neoplastic astrocytes in direct contact, even in low amounts in co-culture, elicited drug resistance in GBM. Recent studies showed that non-neoplastic cells can transfer mitochondria along tunneling nanotubes (TNT) and rescue damaged target cancer cells. In these studies, we explored TNT formation and mitochondrial transfer using 2D and 3D in vitro co-culture models of GBM and astrocytes. TNT formation occurs in glial fibrillary acidic protein (GFAP) positive "reactive" astrocytes after 48 h co-culture and the increase of TNT formations was greater in 3D hyaluronic acid-gelatin based hydrogel models. This study shows that human astrocytes in the tumour microenvironment, both in 2D and 3D in vitro co-culture models, could form TNT connections with GBM cells. We postulate that the association on TNT delivery non-neoplastic mitochondria via a TNT connection may be related to GBM drug response as well as proliferation and migration.