From bulk, single-cell to spatial RNA sequencing

Integrating bulk and single-cell sequencing reveals metastasis-associated CAFs in head and neck squamous cell carcinoma

AIMS

Cancer-associated fibroblasts (CAFs) have been shown to promote the metastasis of head and neck squamous cell carcinoma (HNSCC), but the underlying mechanisms remain unclear. The purpose of this study is to identify gene in CAFs that are associated with metastasis and to preliminarily validate its impact on the metastasis of HNSCC.

MATERIALS AND METHODS

Scissor analysis was utilized to process single-cell and bulk RNA sequencing datasets, identifying genes associated with the metastasis of HNSCC through differential gene expression analysis. A risk model was constructed using LASSO regression analysis. Quantitative real time-PCR and Western blot were employed to measure mRNA and protein expressions, respectively. Multiplex immunohistochemistry (mIHC) was used to assess protein expression in CAFs. siRNA was utilized to achieve gene knockdown. CCK-8 and Transwell assays were employed to evaluate the biological characteristics of HNSCC cells.

KEY FINDINGS

Compare to the nonmetastatic primary CAFs (nmCAFs), tissue inhibitors of metalloproteinase-1 (TIMP1) was founded to be overexpressed in the cells and tissues of metastatic primary CAFs (mCAFs). Knocking down TIMP1 in CAFs can signifucantly inhibit the proliferation, invasion, and migration of HNSCC cells.

SIGNIFICANCE

CAFs facilitate HNSCC cell metastasis by upregulating TIMP1, highlighting TIMP1 as a potential therapeutic target in HNSCC metastasis management.

Targeting BMP2 for therapeutic strategies against hepatocellular carcinoma

OBJECTIVES

This study aimed to investigate the role of BMP2 in hepatocellular carcinoma (HCC) growth and metastasis using a dual approach combining single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq.

METHODS

scRNA-seq data from the GEO database and bulk RNA-seq data from the TCGA database were analyzed. Differentially expressed marker genes of endothelial cells were identified and analyzed using enrichment analysis, PPI analysis, correlation analysis, and GSEA. In vitro, experiments were conducted using the Huh-7 HCC cell line, and in vivo, models of HCC growth and metastasis were established by knocking down BMP2.

RESULTS

The scRNA-seq analysis identified BMP2 as a key marker gene in endothelial cells of HCC samples. Elevated BMP2 expression correlated with poor prognosis in HCC. In vitro experiments showed that silencing BMP2 inhibited the proliferation, migration, and invasion of liver cancer cells. In vivo studies confirmed increased BMP2 expression in HCC tissues, promoting angiogenesis and HCC growth.

CONCLUSION

This study highlights the role of BMP2 in tumor angiogenesis and HCC progression. Targeting BMP2 could be a promising therapeutic strategy against HCC.

Tissue gene expression profiles and communication networks inform candidate blood biomarker identification in psoriasis and atopic dermatitis

Overlapping clinical and pathomechanistic features can complicate the diagnosis and treatment of inflammatory skin diseases, including psoriasis and atopic dermatitis (AD). Spatial transcriptomics allows the identification of disease- and cell-specific molecular signatures that may advance biomarker development and future treatments. This study identified transcriptional signatures in keratinocytes and sub-basal CD4+ and CD8+ T lymphocytes from patients with psoriasis and AD. In silico prediction of ligand:receptor interactions delivered key signalling pathways (interferon, effector T cells, stroma cell and matrix biology, neuronal development, etc.). Targeted validation of selected transcripts, including CCL22, RELB, and JUND, in peripheral blood T cells suggests the chosen approach as a promising tool also in other inflammatory diseases. Psoriasis and AD are characterized by transcriptional dysregulation in T cells and keratinocytes that may be targeted therapeutically. Spatial transcriptomics is a valuable tool in the search for molecular signatures that can be used as biomarkers and/or therapeutic targets.

Integrated analysis of multiple transcriptomic approaches and machine learning integration algorithms reveals high endothelial venules as a prognostic immune-related biomarker in bladder cancer

BACKGROUND

Despite the availability of established surgical and chemotherapy options, the treatment of bladder cancer (BCa) patients remains challenging. While immunotherapy has emerged as a promising approach, its benefits are limited to a subset of patients. The exploration of additional targets to enhance the efficacy of immunotherapy is a valuable research direction.

METHOD

High endothelial venules (HEV) ssGSEA analysis was conducted using BEST. Through the utilization of R packages Limma, Seurat, SingleR, and Harmony, analyses were performed on spatial transcriptomics, bulk RNA-sequencing (bulk RNA-seq), and single-cell RNA sequencing (scRNA-seq) data, yielding HEV-related genes (HEV.RGs). Molecular subtyping analysis based on HEV.RGs was conducted using R package MOVICS, and various machine learning-integrated algorithm was employed to construct prognostic model. LDLRAD3 was validated through subcutaneous tumor formation in mice, HEV induction, Western blot, and qPCR.

RESULTS

A correlation between higher HEV levels and improved immune response and prognosis was revealed by HEV ssGSEA analysis in BCa patients receiving immunotherapy. HEV.RGs were identified in subsequent transcriptomic analyses. Based on these genes, BCa patients were stratified into two molecular clusters with distinct survival and immune infiltration patterns using various clustering-integrated algorithm. Prognostic model was developed using multiple machine learning-integrated algorithm. Low LDLRAD3 expression may promote HEV generation, leading to enhanced immunotherapy efficacy, as suggested by bulk RNA-seq, scRNA-seq analyses, and experimental validation of LDLRAD3.

CONCLUSIONS

HEV served as a predictive factor for immune response and prognosis in BCa patients receiving immunotherapy. LDLRAD3 represented a potential target for HEV induction and enhancing the efficacy of immunotherapy.

Integrated multi-omics with machine learning to uncover the intricacies of kidney disease

The development of omics technologies has driven a profound expansion in the scale of biological data and the increased complexity in internal dimensions, prompting the utilization of machine learning (ML) as a powerful toolkit for extracting knowledge and understanding underlying biological patterns. Kidney disease represents one of the major growing global health threats with intricate pathogenic mechanisms and a lack of precise molecular pathology-based therapeutic modalities. Accordingly, there is a need for advanced high-throughput approaches to capture implicit molecular features and complement current experiments and statistics. This review aims to delineate strategies for integrating multi-omics data with appropriate ML methods, highlighting key clinical translational scenarios, including predicting disease progression risks to improve medical decision-making, comprehensively understanding disease molecular mechanisms, and practical applications of image recognition in renal digital pathology. Examining the benefits and challenges of current integration efforts is expected to shed light on the complexity of kidney disease and advance clinical practice.

Identifying potential therapeutic targets in lung adenocarcinoma: a multi-omics approach integrating bulk and single-cell RNA sequencing with Mendelian randomization

Our research aimed to identify new therapeutic targets for Lung adenocarcinoma (LUAD), a major subtype of non-small cell lung cancer known for its low 5-year survival rate of 22%. By employing a comprehensive methodological approach, we analyzed bulk RNA sequencing data from 513 LUAD and 59 non-tumorous tissues, identifying 2,688 differentially expressed genes. Using Mendelian randomization (MR), we identified 74 genes with strong evidence for a causal effect on risk of LUAD. Survival analysis on these genes revealed significant differences in survival rates for 13 of them. Our pathway enrichment analysis highlighted their roles in immune response and cell communication, deepening our understanding. We also utilized single-cell RNA sequencing (scRNA-seq) to uncover cell type-specific gene expression patterns within LUAD, emphasizing the tumor microenvironment's heterogeneity. Pseudotime analysis further assisted in assessing the heterogeneity of tumor cell populations. Additionally, protein-protein interaction (PPI) network analysis was conducted to evaluate the potential druggability of these identified genes. The culmination of our efforts led to the identification of five genes (tier 1) with the most compelling evidence, including SECISBP2L, PRCD, SMAD9, C2orf91, and HSD17B13, and eight genes (tier 2) with convincing evidence for their potential as therapeutic targets.

Precision oncology: current and future platforms for treatment selection

Genomic profiling of hundreds of cancer-associated genes is now a component of routine cancer care. DNA sequencing can identify mutations, mutational signatures, and structural alterations predictive of therapy response and assess for heritable cancer risk, but it has been less useful for identifying predictive biomarkers of sensitivity to cytotoxic chemotherapies, antibody drug conjugates, and immunotherapies. The clinical adoption of molecular profiling platforms such as RNA sequencing better suited to identifying those patients most likely to respond to immunotherapies and drug combinations will be critical to expanding the benefits of precision oncology. This review discusses the potential advantages of innovative molecular and functional profiling platforms designed to replace or complement targeted DNA sequencing and the major hurdles to their clinical adoption.

Single-cell RNA sequencing in ovarian cancer: revealing new perspectives in the tumor microenvironment

Single-cell sequencing technology has emerged as a pivotal tool for unraveling the complexities of the ovarian tumor microenvironment (TME), which is characterized by its cellular heterogeneity and intricate cell-to-cell interactions. Ovarian cancer (OC), known for its high lethality among gynecologic malignancies, presents significant challenges in treatment and diagnosis, partly due to the complexity of its TME. The application of single-cell sequencing in ovarian cancer research has enabled the detailed characterization of gene expression profiles at the single-cell level, shedding light on the diverse cell populations within the TME, including cancer cells, stromal cells, and immune cells. This high-resolution mapping has been instrumental in understanding the roles of these cells in tumor progression, invasion, metastasis, and drug resistance. By providing insight into the signaling pathways and cell-to-cell communication mechanisms, single-cell sequencing facilitates the identification of novel therapeutic targets and the development of personalized medicine approaches. This review summarizes the advancement and application of single-cell sequencing in studying the stromal components and the broader TME in OC, highlighting its implications for improving diagnosis, treatment strategies, and understanding of the disease's underlying biology.

Application of spatial-omics to the classification of kidney biopsy samples in transplantation

Improvement of long-term outcomes through targeted treatment is a primary concern in kidney transplant medicine. Currently, the validation of a rejection diagnosis and subsequent treatment depends on the histological assessment of allograft biopsy samples, according to the Banff classification system. However, the lack of (early) disease-specific tissue markers hinders accurate diagnosis and thus timely intervention. This challenge mainly results from an incomplete understanding of the pathophysiological processes underlying late allograft failure. Integration of large-scale multimodal approaches for investigating allograft biopsy samples might offer new insights into this pathophysiology, which are necessary for the identification of novel therapeutic targets and the development of tailored immunotherapeutic interventions. Several omics technologies - including transcriptomic, proteomic, lipidomic and metabolomic tools (and multimodal data analysis strategies) - can be applied to allograft biopsy investigation. However, despite their successful application in research settings and their potential clinical value, several barriers limit the broad implementation of many of these tools into clinical practice. Among spatial-omics technologies, mass spectrometry imaging, which is under-represented in the transplant field, has the potential to enable multi-omics investigations that might expand the insights gained with current clinical analysis technologies.

Role of gut microbiota in the pathogenesis of castration-resistant prostate cancer: a comprehensive study using sequencing and animal models

CRPC remains a significant challenge in prostate cancer research. We aimed to elucidate the role of gut microbiota and its specific mechanisms in CRPC using a multidisciplinary approach. We analyzed 16S rRNA sequencing data from mouse fecal samples, revealing substantial differences in gut microbiota composition between CRPC and castration-sensitive prostate cancer mice, particularly in Firmicutes and Bacteroidetes. Functional analysis suggested different bacteria may influence CRPC via the α-linolenic acid metabolism pathway. In vivo, experiments utilizing mouse models and fecal microbiota transplantation (FMT) demonstrated that FMT from healthy control mice could decelerate tumor growth in CRPC mice, reduce TNF-α levels, and inhibit the activation of the TLR4/MyD88/NF-κB signaling pathway. Transcriptome sequencing identified crucial genes and pathways, with rescue experiments confirming the gut microbiota's role in modulating CRPC progression through the TLR4/MyD88/NF-κB pathway. The activation of this pathway by TNF-α has been corroborated by in vitro cell experiments, indicating its role in promoting prostate cancer cell proliferation, migration, and invasion while inhibiting apoptosis. Gut microbiota dysbiosis may promote CRPC development through TNF-α activation of the TLR4/MyD88/NF-κB signaling pathway, potentially linked to α-linolenic acid metabolism.

The endometrial transcriptome of infertile women with and without implantation failure

INTRODUCTION

Implantation failure after transferring morphologically "good-quality" embryos in in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) may be explained by impaired endometrial receptivity. Analyzing the endometrial transcriptome analysis may reveal the underlying processes and could help in guiding prognosis and using targeted interventions for infertility. This exploratory study investigated whether the endometrial transcriptome profile was associated with short-term or long-term implantation outcomes (ie success or failure).

MATERIAL AND METHODS

Mid-luteal phase endometrial biopsies of 107 infertile women with one full failed IVF/ICSI cycle, obtained within an endometrial scratching trial, were subjected to RNA-sequencing and differentially expressed genes analysis with covariate adjustment (age, body mass index, luteinizing hormone [LH]-day). Endometrial transcriptomes were compared between implantation failure and success groups in the short term (after the second fresh IVF/ICSI cycle) and long term (including all fresh and frozen cycles within 12 months). The short-term analysis included 85/107 women (33 ongoing pregnancy vs 52 no pregnancy), excluding 22/107 women. The long-term analysis included 46/107 women (23 'fertile' group, ie infertile women with a live birth after ≤3 embryos transferred vs 23 recurrent implantation failure group, ie no live birth after ≥3 good quality embryos transferred), excluding 61/107 women not fitting these categories. As both analyses drew from the same pool of 107 samples, there was some sample overlap. Additionally, cell type enrichment scores and endometrial receptivity were analyzed, and an endometrial development pseudo-timeline was constructed to estimate transcriptomic deviations from the optimum receptivity day (LH + 7), denoted as ΔWOI (window of implantation).

RESULTS

There were no significantly differentially expressed genes between implantation failure and success groups in either the short-term or long-term analyses. Principal component analysis initially showed two clusters in the long-term analysis, unrelated to clinical phenotype and no longer distinct following covariate adjustment. Cell type enrichment scores did not differ significantly between groups in both analyses. However, endometrial receptivity analysis demonstrated a potentially significant displacement of the WOI in the non-pregnant group compared with the ongoing pregnant group in the short-term analysis.

CONCLUSIONS

No distinct endometrial transcriptome profile was associated with either implantation failure or success in infertile women. However, there may be differences in the extent to which the WOI is displaced.

DeepGSEA: explainable deep gene set enrichment analysis for single-cell transcriptomic data

MOTIVATION

Gene set enrichment (GSE) analysis allows for an interpretation of gene expression through pre-defined gene set databases and is a critical step in understanding different phenotypes. With the rapid development of single-cell RNA sequencing (scRNA-seq) technology, GSE analysis can be performed on fine-grained gene expression data to gain a nuanced understanding of phenotypes of interest. However, with the cellular heterogeneity in single-cell gene profiles, current statistical GSE analysis methods sometimes fail to identify enriched gene sets. Meanwhile, deep learning has gained traction in applications like clustering and trajectory inference in single-cell studies due to its prowess in capturing complex data patterns. However, its use in GSE analysis remains limited, due to interpretability challenges.

RESULTS

In this paper, we present DeepGSEA, an explainable deep gene set enrichment analysis approach which leverages the expressiveness of interpretable, prototype-based neural networks to provide an in-depth analysis of GSE. DeepGSEA learns the ability to capture GSE information through our designed classification tasks, and significance tests can be performed on each gene set, enabling the identification of enriched sets. The underlying distribution of a gene set learned by DeepGSEA can be explicitly visualized using the encoded cell and cellular prototype embeddings. We demonstrate the performance of DeepGSEA over commonly used GSE analysis methods by examining their sensitivity and specificity with four simulation studies. In addition, we test our model on three real scRNA-seq datasets and illustrate the interpretability of DeepGSEA by showing how its results can be explained.

AVAILABILITY AND IMPLEMENTATION

https://github.com/Teddy-XiongGZ/DeepGSEA.

A new atlas to study embryonic cell types in Xenopus

This paper introduces a single-cell atlas for pivotal developmental stages in Xenopus, encompassing gastrulation, neurulation, and early tailbud. Notably surpassing its predecessors, the new atlas enhances gene mapping, read counts, and gene/cell type nomenclature. Leveraging the latest Xenopus tropicalis genome version, alongside advanced alignment pipelines and machine learning for cell type assignment, this release maintains consistency with previous cell type annotations while rectifying nomenclature issues. Employing an unbiased approach for cell type assignment proves especially apt for embryonic contexts, given the considerable number of non-terminally differentiated cell types. An alternative cell type attribution here adopts a fuzzy, non-deterministic stance, capturing the transient nature of early embryo progenitor cells by presenting an ensemble of types in superposition. The value of the new resource is emphasized through numerous examples, with a focus on previously unexplored germ cell populations where we uncover novel transcription onset features. Offering interactive exploration via a user-friendly web portal and facilitating complete data downloads, this atlas serves as a comprehensive and accessible reference.

Assessing RNA-Seq Workflow Methodologies Using Shannon Entropy

RNA-seq faces persistent challenges due to the ongoing, expanding array of data processing workflows, none of which have yet achieved standardization to date. It is imperative to determine which method most effectively preserves biological facts. Here, we used Shannon entropy as a tool for depicting the biological status of a system. Thus, we assessed the measurement of Shannon entropy by several RNA-seq workflow approaches, such as DESeq2 and edgeR, but also by combining nine normalization methods with log2 fold change on paired samples of TCGA RNA-seq representing datasets of 515 patients and spanning 12 different cancer types with 5-year overall survival rates ranging from 20% to 98%. Our analysis revealed that TPM, RLE, and TMM normalization, coupled with a threshold of log2 fold change ≥1, for identifying differentially expressed genes, yielded the best results. We propose that Shannon entropy can serve as an objective metric for refining the optimization of RNA-seq workflows and mRNA sequencing technologies.

Cross-species transcriptomics identifies obesity associated genes between human and mouse studies

BACKGROUND

Fundamentally defined by an imbalance in energy consumption and energy expenditure, obesity is a significant risk factor of several musculoskeletal conditions including osteoarthritis (OA). High-fat diets and sedentary lifestyle leads to increased adiposity resulting in systemic inflammation due to the endocrine properties of adipose tissue producing inflammatory cytokines and adipokines. We previously showed serum levels of specific adipokines are associated with biomarkers of bone remodelling and cartilage volume loss in knee OA patients. Whilst more recently we find the metabolic consequence of obesity drives the enrichment of pro-inflammatory fibroblast subsets within joint synovial tissues in obese individuals compared to those of BMI defined 'health weight'. As such this present study identifies obesity-associated genes in OA joint tissues which are conserved across species and conditions.

METHODS

The study utilised 6 publicly available bulk and single-cell transcriptomic datasets from human and mice studies downloaded from Gene Expression Omnibus (GEO). Machine learning models were employed to model and statistically test datasets for conserved gene expression profiles. Identified genes were validated in OA tissues from obese and healthy weight individuals using quantitative PCR method (N = 38). Obese and healthy-weight patients were categorised by BMI > 30 and BMI between 18 and 24.9 respectively. Informed consent was obtained from all study participants who were scheduled to undergo elective arthroplasty.

RESULTS

Principal component analysis (PCA) was used to investigate the variations between classes of mouse and human data which confirmed variation between obese and healthy populations. Differential gene expression analysis filtered on adjusted p-values of p < 0.05, identified differentially expressed genes (DEGs) in mouse and human datasets. DEGs were analysed further using area under curve (AUC) which identified 12 genes. Pathway enrichment analysis suggests these genes were involved in the biosynthesis and elongation of fatty acids and the transport, oxidation, and catabolic processing of lipids. qPCR validation found the majority of genes showed a tendency to be upregulated in joint tissues from obese participants. Three validated genes, IGFBP2 (p = 0.0363), DOK6 (0.0451) and CASP1 (0.0412) were found to be significantly different in obese joint tissues compared to lean-weight joint tissues.

CONCLUSIONS

The present study has employed machine learning models across several published obesity datasets to identify obesity-associated genes which are validated in joint tissues from OA. These results suggest obesity-associated genes are conserved across conditions and may be fundamental in accelerating disease in obese individuals. Whilst further validations and additional conditions remain to be tested in this model, identifying obesity-associated genes in this way may serve as a global aid for patient stratification giving rise to the potential of targeted therapeutic interventions in such patient subpopulations.

Long-read single-cell sequencing reveals the transcriptional landscape of spermatogenesis in obstructive azoospermia and Sertoli cell-only patients

BACKGROUND

High-throughput single-cell RNA sequencing (scRNA-seq) is widely used in spermatogenesis. However, it only reveals short reads in germ and somatic cells, limiting the discovery of novel transcripts and genes.

AIM

This study shows the long-read transcriptional landscape of spermatogenesis in obstructive azoospermia (OA) and Sertoli cell-only patients.

DESIGN

Single cells were isolated from testicular biopsies of OA and non-obstructive azoospermia (NOA) patients. Cell culture was identified by comparing PacBio long-read single-cell sequencing (OA n = 3, NOA n = 3) with short-read scRNA-seq (OA n = 6, NOA n = 6). Ten germ cell types and eight somatic cell types were classified based on known markers.

METHODS

PacBio long-read single-cell sequencing, short-read scRNA-seq, polymerase chain reaction.

RESULTS

A total of 130 426 long-read transcripts (100 517 novel transcripts and 29 909 known transcripts) and 49 508 long-read transcripts (26 002 novel transcripts and 23 506 known transcripts) have been detected in OA and NOA patients, respectively. Moreover, 36 373 and 1642 new genes are identified in OA and NOA patients, respectively. Importantly, specific expressions of long-read transcripts were detected in germ and stomatic cells during normal spermatogenesis.

CONCLUSION

We have identified total full-length transcripts in OA and NOA, and new genes were found. Furthermore, specific expressed full-length transcripts were detected, and the genomic structure of transcripts was mapped in different cell types. These findings may provide valuable information on human spermatogenesis and the treatment of male infertility.

Considerations for the development of iPSC-derived cell therapies: a review of key challenges by the JSRM-ISCT iPSC Committee

Since their first production in 2007, human induced pluripotent stem cells (iPSCs) have provided a novel platform for the development of various cell therapies targeting a spectrum of diseases, ranging from rare genetic eye disorders to cancer treatment. However, several challenges must be tackled for iPSC-based cell therapy to enter the market and achieve broader global adoption. This white paper, authored by the Japanese Society for Regenerative Medicine (JSRM) - International Society for Cell Therapy (ISCT) iPSC Committee delves into the hurdles encountered in the pursuit of safe and economically viable iPSC-based therapies, particularly from the standpoint of the cell therapy industry. It discusses differences in global guidelines and regulatory frameworks, outlines a series of quality control tests required to ensure the safety of the cell therapy, and provides details and important considerations around cost of goods (COGs), including the impact of automated advanced manufacturing.

How should we sample snail microbiomes?

Background

The microbiome is increasingly recognized to shape many aspects of its host biology and is a key determinant of health and disease. The microbiome may influence transmission of pathogens by their vectors, such as mosquitoes or aquatic snails. We previously sequenced the bacterial 16S V4 ribosomal DNA of the hemolymph (blood) of Biomphalaria spp. snails, one of the vectors of the human blood fluke schistosome. We showed that snail hemolymph harbored an abundant and diverse microbiome. This microbiome is distinct from the water environment and can discriminate snail species and populations. As hemolymph bathes snail organs, we then investigated the heterogeneity of the microbiome in these organs.

Results

We dissected ten snails for each of two different species (B. alexandrina and B. glabrata) and collected their organs (ovotestis, hepatopancreas, gut, and stomach). We also ground in liquid nitrogen four whole snails of each species. We sampled the water in which the snails were living (environmental controls). Sequencing the 16S V4 rDNA revealed organ-specific microbiomes. These microbiomes harbored a lower diversity than the hemolymph microbiome, and the whole-snail microbiome. The organ microbiomes tend to cluster by physiological function. In addition, we showed that the whole-snail microbiome is more similar to hemolymph microbiome.

Conclusions

These results are critical for future work on snail microbiomes and show the necessity of sampling individual organ microbiomes to provide a complete description of snail microbiomes.

Spatial transcriptomics in cancer research and potential clinical impact: a narrative review

Spatial transcriptomics (ST) provides novel insights into the tumor microenvironment (TME). ST allows the quantification and illustration of gene expression profiles in the spatial context of tissues, including both the cancer cells and the microenvironment in which they are found. In cancer research, ST has already provided novel insights into cancer metastasis, prognosis, and immunotherapy responsiveness. The clinical precision oncology application of next-generation sequencing (NGS) and RNA profiling of tumors relies on bulk methods that lack spatial context. The ability to preserve spatial information is now possible, as it allows us to capture tumor heterogeneity and multifocality. In this narrative review, we summarize precision oncology, discuss tumor sequencing in the clinic, and review the available ST research methods, including seqFISH, MERFISH (Vizgen), CosMx SMI (NanoString), Xenium (10x), Visium (10x), Stereo-seq (STOmics), and GeoMx DSP (NanoString). We then review the current ST literature with a focus on solid tumors organized by tumor type. Finally, we conclude by addressing an important question: how will spatial transcriptomics ultimately help patients with cancer?

Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine

Aging and regeneration represent complex biological phenomena that have long captivated the scientific community. To fully comprehend these processes, it is essential to investigate molecular dynamics through a lens that encompasses both spatial and temporal dimensions. Conventional omics methodologies, such as genomics and transcriptomics, have been instrumental in identifying critical molecular facets of aging and regeneration. However, these methods are somewhat limited, constrained by their spatial resolution and their lack of capacity to dynamically represent tissue alterations. The advent of emerging spatiotemporal multi-omics approaches, encompassing transcriptomics, proteomics, metabolomics, and epigenomics, furnishes comprehensive insights into these intricate molecular dynamics. These sophisticated techniques facilitate accurate delineation of molecular patterns across an array of cells, tissues, and organs, thereby offering an in-depth understanding of the fundamental mechanisms at play. This review meticulously examines the significance of spatiotemporal multi-omics in the realms of aging and regeneration research. It underscores how these methodologies augment our comprehension of molecular dynamics, cellular interactions, and signaling pathways. Initially, the review delineates the foundational principles underpinning these methods, followed by an evaluation of their recent applications within the field. The review ultimately concludes by addressing the prevailing challenges and projecting future advancements in the field. Indubitably, spatiotemporal multi-omics are instrumental in deciphering the complexities inherent in aging and regeneration, thus charting a course toward potential therapeutic innovations.

SpatialPrompt: spatially aware scalable and accurate tool for spot deconvolution and domain identification in spatial transcriptomics

Efficiently mapping of cell types in situ remains a major challenge in spatial transcriptomics. Most spot deconvolution tools ignore spatial coordinate information and perform extremely slow on large datasets. Here, we introduce SpatialPrompt, a spatially aware and scalable tool for spot deconvolution and domain identification. SpatialPrompt integrates gene expression, spatial location, and single-cell RNA sequencing (scRNA-seq) dataset as reference to accurately infer cell-type proportions of spatial spots. SpatialPrompt uses non-negative ridge regression and graph neural network to efficiently capture local microenvironment information. Our extensive benchmarking analysis on Visium, Slide-seq, and MERFISH datasets demonstrated superior performance of SpatialPrompt over 15 existing tools. On mouse hippocampus dataset, SpatialPrompt achieves spot deconvolution and domain identification within 2 minutes for 50,000 spots. Overall, domain identification using SpatialPrompt was 44 to 150 times faster than existing methods. We build a database housing 40 plus curated scRNA-seq datasets for seamless integration with SpatialPrompt for spot deconvolution.

Integrating spatial transcriptomics and bulk RNA-seq: predicting gene expression with enhanced resolution through graph attention networks

Spatial transcriptomics data play a crucial role in cancer research, providing a nuanced understanding of the spatial organization of gene expression within tumor tissues. Unraveling the spatial dynamics of gene expression can unveil key insights into tumor heterogeneity and aid in identifying potential therapeutic targets. However, in many large-scale cancer studies, spatial transcriptomics data are limited, with bulk RNA-seq and corresponding Whole Slide Image (WSI) data being more common (e.g. TCGA project). To address this gap, there is a critical need to develop methodologies that can estimate gene expression at near-cell (spot) level resolution from existing WSI and bulk RNA-seq data. This approach is essential for reanalyzing expansive cohort studies and uncovering novel biomarkers that have been overlooked in the initial assessments. In this study, we present STGAT (Spatial Transcriptomics Graph Attention Network), a novel approach leveraging Graph Attention Networks (GAT) to discern spatial dependencies among spots. Trained on spatial transcriptomics data, STGAT is designed to estimate gene expression profiles at spot-level resolution and predict whether each spot represents tumor or non-tumor tissue, especially in patient samples where only WSI and bulk RNA-seq data are available. Comprehensive tests on two breast cancer spatial transcriptomics datasets demonstrated that STGAT outperformed existing methods in accurately predicting gene expression. Further analyses using the TCGA breast cancer dataset revealed that gene expression estimated from tumor-only spots (predicted by STGAT) provides more accurate molecular signatures for breast cancer sub-type and tumor stage prediction, and also leading to improved patient survival and disease-free analysis. Availability: Code is available at https://github.com/compbiolabucf/STGAT.

Anticancer Effects of Wild Baicalin on Hepatocellular Carcinoma: Downregulation of AKR1B10 and PI3K/AKT Signaling Pathways

Introduction

Hepatocellular carcinoma (HCC) is a common and deadly malignancy. Traditional Chinese medicine, such as the compound Astragalus (wild Baicalin), has shown promise in improving outcomes for HCC patients. This study aimed to investigate the effects of wild Baicalin on the human hepatoma cell line HepG2 and elucidate the underlying mechanisms, particularly the role of the AKR1B10 and PI3K/AKT signaling pathways.

Methods

HepG2 cells were treated with varying concentrations of wild Baicalin. Cell proliferation, apoptosis, migration, invasion, and cell cycle were evaluated using CCK-8, flow cytometry, scratch, Transwell, and clonogenic assays, respectively. Transcriptome sequencing was performed to analyze gene expression changes induced by wild Baicalin. Differentially expressed genes were identified and analyzed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The expression of AKR1B10 and PI3K was validated by qPCR.

Results

Wild Baicalin inhibited HepG2 cell proliferation, induced apoptosis, suppressed migration and invasion, and caused cell cycle arrest in a dose-dependent manner. Transcriptome sequencing revealed 1202 differentially expressed genes, including 486 upregulated and 716 downregulated genes. GO analysis indicated that biological processes were pivotal in the anticancer mechanism of wild Baicalin, while KEGG analysis identified metabolic pathways as the most significantly regulated. AKR1B10 and PI3K, key genes in metabolic pathways, were downregulated by wild Baicalin, which was confirmed by qPCR.

Discussion

The findings suggest that wild Baicalin exhibits potent anticancer effects against HepG2 cells by inducing apoptosis, inhibiting proliferation, migration, and invasion, and causing cell cycle arrest. The regulatory effects of wild Baicalin on the AKR1B10 and PI3K/AKT signaling pathways appear to be critical for its inhibitory effects on HCC cell proliferation. These results provide new insights into the mechanism of action of wild Baicalin and support its potential as a therapeutic approach for HCC treatment.

A comprehensive single-cell breast tumor atlas defines epithelial and immune heterogeneity and interactions predicting anti-PD-1 therapy response

We present an integrated single-cell RNA sequencing atlas of the primary breast tumor microenvironment (TME) containing 236,363 cells from 119 biopsy samples across eight datasets. In this study, we leverage this resource for multiple analyses of immune and cancer epithelial cell heterogeneity. We define natural killer (NK) cell heterogeneity through six subsets in the breast TME. Because NK cell heterogeneity correlates with epithelial cell heterogeneity, we characterize epithelial cells at the level of single-gene expression, molecular subtype, and 10 categories reflecting intratumoral transcriptional heterogeneity. We develop InteractPrint, which considers how cancer epithelial cell heterogeneity influences cancer-immune interactions. We use T cell InteractPrint to predict response to immune checkpoint inhibition (ICI) in two breast cancer clinical trials testing neoadjuvant anti-PD-1 therapy. T cell InteractPrint was predictive of response in both trials versus PD-L1 (AUC = 0.82, 0.83 vs. 0.50, 0.72). This resource enables additional high-resolution investigations of the breast TME.

Detecting the effect of genetic diversity on brain composition in an Alzheimer's disease mouse model

Alzheimer's disease (AD) is broadly characterized by neurodegeneration, pathology accumulation, and cognitive decline. There is considerable variation in the progression of clinical symptoms and pathology in humans, highlighting the importance of genetic diversity in the study of AD. To address this, we analyze cell composition and amyloid-beta deposition of 6- and 14-month-old AD-BXD mouse brains. We utilize the analytical QUINT workflow- a suite of software designed to support atlas-based quantification, which we expand to deliver a highly effective method for registering and quantifying cell and pathology changes in diverse disease models. In applying the expanded QUINT workflow, we quantify near-global age-related increases in microglia, astrocytes, and amyloid-beta, and we identify strain-specific regional variation in neuron load. To understand how individual differences in cell composition affect the interpretation of bulk gene expression in AD, we combine hippocampal immunohistochemistry analyses with bulk RNA-sequencing data. This approach allows us to categorize genes whose expression changes in response to AD in a cell and/or pathology load-dependent manner. Ultimately, our study demonstrates the use of the QUINT workflow to standardize the quantification of immunohistochemistry data in diverse mice, - providing valuable insights into regional variation in cellular load and amyloid deposition in the AD-BXD model.

Can long-read sequencing tackle the barriers, which the next-generation could not? A review

The large-scale heterogeneity of genetic diseases necessitated the deeper examination of nucleotide sequence alterations enhancing the discovery of new targeted drug attack points. The appearance of new sequencing techniques was essential to get more interpretable genomic data. In contrast to the previous short-reads, longer lengths can provide a better insight into the potential health threatening genetic abnormalities. Long-reads offer more accurate variant identification and genome assembly methods, indicating advances in nucleotide deflect-related studies. In this review, we introduce the historical background of sequencing technologies and show their benefits and limits, as well. Furthermore, we highlight the differences between short- and long-read approaches, including their unique advances and difficulties in methodologies and evaluation. Additionally, we provide a detailed description of the corresponding bioinformatics and the current applications.

Identifying the miRNA-gene networks contributes to exploring paravertebral muscle degeneration's underlying pathogenesis and therapy strategy

Low back pain (LBP) is a worldwide problem with public health. Paravertebral muscle degeneration (PMD) is believed to be associated with LBP. Increasing evidence has demonstrated that microRNA (miRNA)-mRNA signaling networks have been implicated in the pathophysiology of diseases. Research suggests that cell death, oxidative stress, inflammatory and immune response, and extracellular matrix (ECM) metabolism are the pathogenesis of PMD; however, the miRNA-mRNA mediated the pathological process of PMD remains elusive. RNA sequencing (RNA-seq) and single cell RNA-seq (scRNA-seq) are invaluable tools for uncovering the functional biology underlying these miRNA and gene expression changes. Using scRNA-seq, we show that multiple immunocytes are presented during PMD, revealing that they may have been implicated with PMD. Additionally, using RNA-seq, we identified 76 differentially expressed genes (DEGs) and 106 differentially expressed miRNAs (DEMs), among which IL-24 and CCDC63 were the top upregulated and downregulated genes in PMD. Comprehensive bioinformatics analyses, including Venn diagrams, differential expression, functional enrichment, and protein-protein interaction analysis, were then conducted to identify six ferroptosis-related DEGs, two oxidative stress-related DEGs, eleven immunity-related DEGs, five ECM-related DEGs, among which AKR1C2/AKR1C3/SIRT1/ALB/IL-24 belong to inflammatory genes. Furthermore, 67 DEMs were predicted to be upstream miRNAs of 25 key DEGs by merging RNA-seq, TargetScan, and mirDIP databases. Finally, a miRNA-gene network was constructed using Cytoscape software and an alluvial plot. ROC curve analysis unveiled multiple key DEGs with the high clinical diagnostic value, providing novel approaches for diagnosing and treating PMD diseases.

A Spatial Transcriptomics Browser for Discovering Gene Expression Landscapes across Microscopic Tissue Sections

A crucial feature of life is its spatial organization and compartmentalization on the molecular, cellular, and tissue levels. Spatial transcriptomics (ST) technology has opened a new chapter of the sequencing revolution, emerging rapidly with transformative effects across biology. This technique produces extensive and complex sequencing data, raising the need for computational methods for their comprehensive analysis and interpretation. We developed the ST browser web tool for the interactive discovery of ST images, focusing on different functional aspects such as single gene expression, the expression of functional gene sets, as well as the inspection of the spatial patterns of cell-cell interactions. As a unique feature, our tool applies self-organizing map (SOM) machine learning to the ST data. Our SOM data portrayal method generates individual gene expression landscapes for each spot in the ST image, enabling its downstream analysis with high resolution. The performance of the spatial browser is demonstrated by disentangling the intra-tumoral heterogeneity of melanoma and the microarchitecture of the mouse brain. The integration of machine-learning-based SOM portrayal into an interactive ST analysis environment opens novel perspectives for the comprehensive knowledge mining of the organization and interactions of cellular ecosystems.

DeepDecon accurately estimates cancer cell fractions in bulk RNA-seq data

Understanding the cellular composition of a disease-related tissue is important in disease diagnosis, prognosis, and downstream treatment. Recent advances in single-cell RNA-sequencing (scRNA-seq) technique have allowed the measurement of gene expression profiles for individual cells. However, scRNA-seq is still too expensive to be used for large-scale population studies, and bulk RNA-seq is still widely used in such situations. An essential challenge is to deconvolve cellular composition for bulk RNA-seq data based on scRNA-seq data. Here, we present DeepDecon, a deep neural network model that leverages single-cell gene expression information to accurately predict the fraction of cancer cells in bulk tissues. It provides a refining strategy in which the cancer cell fraction is iteratively estimated by a set of trained models. When applied to simulated and real cancer data, DeepDecon exhibits superior performance compared to existing decomposition methods in terms of accuracy.

Advances in sequencing and omics studies in prostate cancer: unveiling molecular pathogenesis and clinical applications

Background

Prostate cancer (PCa) is one of the most threatening health problems for the elderly males. However, our understanding of the disease has been limited by the research technology for a long time. Recently, the maturity of sequencing technology and omics studies has been accelerating the studies of PCa, establishing themselves as an essential impetus in this field.

Methods

We assessed Web of Science (WoS) database for publications of sequencing and omics studies in PCa on July 3rd, 2023. Bibliometrix was used to conduct ulterior bibliometric analysis of countries/affiliations, authors, sources, publications, and keywords. Subsequently, purposeful large amounts of literature reading were proceeded to analyze research hotspots in this field.

Results

3325 publications were included in the study. Research associated with sequencing and omics studies in PCa had shown an obvious increase recently. The USA and China were the most productive countries, and harbored close collaboration. CHINNAIYAN AM was identified as the most influential author, and CANCER RESEARCH exhibited huge impact in this field. Highly cited publications and their co-citation relationships were used to filtrate literatures for subsequent literature reading. Based on keyword analysis and large amounts of literature reading, 'the molecular pathogenesis of PCa' and 'the clinical application of sequencing and omics studies in PCa' were summarized as two research hotspots in the field.

Conclusion

Sequencing technology had a deep impact on the studies of PCa. Sequencing and omics studies in PCa helped researchers reveal the molecular pathogenesis, and provided new possibilities for the clinical practice of PCa.

Edge-relational window-attentional graph neural network for gene expression prediction in spatial transcriptomics analysis

Spatial transcriptomics (ST), containing gene expression with fine-grained (i.e., different windows) spatial location within tissue samples, has become vital in developing innovative treatments. Traditional ST technology, however, rely on costly specialized commercial equipment. Addressing this, our article aims to creates a cost-effective, virtual ST approach using standard tissue images for gene expression prediction, eliminating the need for expensive equipment. Conventional approaches in this field often overlook the long-distance spatial dependencies between different sample windows or need prior gene expression data. To overcome these limitations, we propose the Edge-Relational Window-Attentional Network (ErwaNet), enhancing gene prediction by capturing both local interactions and global structural information from tissue images, without prior gene expression data. ErwaNet innovatively constructs heterogeneous graphs to model local window interactions and incorporates an attention mechanism for global information analysis. This dual framework not only provides a cost-effective solution for gene expression predictions but also obviates the necessity of prior knowledge gene expression information, a significant advantage in the field of cancer research where it enables a more efficient and accessible analytical paradigm. ErwaNet stands out as a prior-free and easy-to-implement Graph Convolution Network (GCN) method for predicting gene expression from tissue images. Evaluation of the two public breast cancer datasets shows that ErwaNet, without additional information, outperforms the state-of-the-art (SOTA) methods. Code is available at https://github.com/biyecc/ErwaNet.

DISSECT: deep semi-supervised consistency regularization for accurate cell type fraction and gene expression estimation

Cell deconvolution is the estimation of cell type fractions and cell type-specific gene expression from mixed data. An unmet challenge in cell deconvolution is the scarcity of realistic training data and the domain shift often observed in synthetic training data. Here, we show that two novel deep neural networks with simultaneous consistency regularization of the target and training domains significantly improve deconvolution performance. Our algorithm, DISSECT, outperforms competing algorithms in cell fraction and gene expression estimation by up to 14 percentage points. DISSECT can be easily adapted to other biomedical data types, as exemplified by our proteomic deconvolution experiments.

Single-cell analysis revealing the metabolic landscape of prostate cancer

Tumor metabolic reprogramming is a hallmark of cancer development, and targeting metabolic vulnerabilities has been proven to be an effective approach for castration-resistant prostate cancer (CRPC) treatment. Nevertheless, treatment failure inevitably occurs, largely due to cellular heterogeneity, which cannot be deciphered by traditional bulk sequencing techniques. By employing computational pipelines for single-cell RNA sequencing, we demonstrated that epithelial cells within the prostate are more metabolically active and plastic than stromal cells. Moreover, we identified that neuroendocrine (NE) cells tend to have high metabolic rates, which might explain the high demand for nutrients and energy exhibited by neuroendocrine prostate cancer (NEPC), one of the most lethal variants of prostate cancer (PCa). Additionally, we demonstrated through computational and experimental approaches that variation in mitochondrial activity is the greatest contributor to metabolic heterogeneity among both tumor cells and nontumor cells. These results establish a detailed metabolic landscape of PCa, highlight a potential mechanism of disease progression, and emphasize the importance of future studies on tumor heterogeneity and the tumor microenvironment from a metabolic perspective.

Unveiling novel insights in acute myeloid leukemia through single-cell RNA sequencing

Acute myeloid leukemia (AML) is a complex and heterogeneous group of aggressive hematopoietic stem cell disease. The presence of diverse and functionally distinct populations of leukemia cells within the same patient's bone marrow or blood poses a significant challenge in diagnosing and treating AML. A substantial proportion of AML patients demonstrate resistance to induction chemotherapy and a grim prognosis upon relapse. The rapid advance in next generation sequencing technologies, such as single-cell RNA-sequencing (scRNA-seq), has revolutionized our understanding of AML pathogenesis by enabling high-resolution interrogation of the cellular heterogeneity in the AML ecosystem, and their transcriptional signatures at a single-cell level. New studies have successfully characterized the inextricably intertwined interactions among AML cells, immune cells and bone marrow microenvironment and their contributions to the AML development, therapeutic resistance and relapse. These findings have deepened and broadened our understanding the complexity and heterogeneity of AML, which are difficult to detect with bulk RNA-seq. This review encapsulates the burgeoning body of knowledge generated through scRNA-seq, providing the novel insights and discoveries it has unveiled in AML biology. Furthermore, we discuss the potential implications of scRNA-seq in therapeutic opportunities, focusing on immunotherapy. Finally, we highlight the current limitations and future direction of scRNA-seq in the field.

Tumor-agnostic transcriptome-based classifier identifies spatial infiltration patterns of CD8+T cells in the tumor microenvironment and predicts clinical outcome in early-phase and late-phase clinical trials

BACKGROUND

The immune status of a patient's tumor microenvironment (TME) may guide therapeutic interventions with cancer immunotherapy and help identify potential resistance mechanisms. Currently, patients' immune status is mostly classified based on CD8+tumor-infiltrating lymphocytes. An unmet need exists for comparable and reliable precision immunophenotyping tools that would facilitate clinical treatment-relevant decision-making and the understanding of how to overcome resistance mechanisms.

METHODS

We systematically analyzed the CD8 immunophenotype of 2023 patients from 14 phase I-III clinical trials using immunohistochemistry (IHC) and additionally profiled gene expression by RNA-sequencing (RNA-seq). CD8 immunophenotypes were classified by pathologists into CD8-desert, CD8-excluded or CD8-inflamed tumors using CD8 IHC staining in epithelial and stromal areas of the tumor. Using regularized logistic regression, we developed an RNA-seq-based classifier as a surrogate to the IHC-based spatial classification of CD8+tumor-infiltrating lymphocytes in the TME.

RESULTS

The CD8 immunophenotype and associated gene expression patterns varied across indications as well as across primary and metastatic lesions. Melanoma and kidney cancers were among the strongest inflamed indications, while CD8-desert phenotypes were most abundant in liver metastases across all tumor types. A good correspondence between the transcriptome and the IHC-based evaluation enabled us to develop a 92-gene classifier that accurately predicted the IHC-based CD8 immunophenotype in primary and metastatic samples (area under the curve inflamed=0.846; excluded=0.712; desert=0.855). The newly developed classifier was prognostic in The Cancer Genome Atlas (TCGA) data and predictive in lung cancer: patients with predicted CD8-inflamed tumors showed prolonged overall survival (OS) versus patients with CD8-desert tumors (HR 0.88; 95% CI 0.80 to 0.97) across TCGA, and longer OS on immune checkpoint inhibitor administration (phase III OAK study) in non-small-cell lung cancer (HR 0.75; 95% CI 0.58 to 0.97).

CONCLUSIONS

We provide a new precision immunophenotyping tool based on gene expression that reflects the spatial infiltration patterns of CD8+ lymphocytes in tumors. The classifier enables multiplex analyses and is easy to apply for retrospective, reverse translation approaches as well as for prospective patient enrichment to optimize the response to cancer immunotherapy.

Macrophage diversity in human cancers: New insight provided by single-cell resolution and spatial context

M1/M2 paradigm of macrophage plasticity has existed for decades. Now it becomes clear that this dichotomy doesn't adequately reflect the diversity of macrophage phenotypes in tumor microenvironment (TME). Tumor-associated macrophages (TAMs) are a major population of innate immune cells in the TME that promotes tumor cell proliferation, angiogenesis and lymphangiogenesis, invasion and metastatic niche formation, as well as response to anti-tumor therapy. However, the fundamental restriction in therapeutic TAM targeting is the limited knowledge about the specific TAM states in distinct human cancer types. Here we summarized the results of the most recent studies that use advanced technologies (e.g. single-cell RNA sequencing and spatial transcriptomics) allowing to decipher novel functional subsets of TAMs in numerous human cancers. The transcriptomic profiles of these TAM subsets and their clinical significance were described. We emphasized the characteristics of specific TAM subpopulations - TREM2+, SPP1+, MARCO+, FOLR2+, SIGLEC1+, APOC1+, C1QC+, and others, which have been most extensively characterized in several cancers, and are associated with cancer prognosis. Spatial transcriptomics technologies defined specific spatial interactions between TAMs and other cell types, especially fibroblasts, in tumors. Spatial transcriptomics methods were also applied to identify markers of immunotherapy response, which are expressed by macrophages or in the macrophage-abundant regions. We highlighted the perspectives for novel techniques that utilize spatial and single cell resolution in investigating new ligand-receptor interactions for effective immunotherapy based on TAM-targeting.

Split Pool Ligation-based Single-cell Transcriptome sequencing (SPLiT-seq) data processing pipeline comparison

BACKGROUND

Single-cell sequencing techniques are revolutionizing every field of biology by providing the ability to measure the abundance of biological molecules at a single-cell resolution. Although single-cell sequencing approaches have been developed for several molecular modalities, single-cell transcriptome sequencing is the most prevalent and widely applied technique. SPLiT-seq (split-pool ligation-based transcriptome sequencing) is one of these single-cell transcriptome techniques that applies a unique combinatorial-barcoding approach by splitting and pooling cells into multi-well plates containing barcodes. This unique approach required the development of dedicated computational tools to preprocess the data and extract the count matrices. Here we compare eight bioinformatic pipelines (alevin-fry splitp, LR-splitpipe, SCSit, splitpipe, splitpipeline, SPLiTseq-demultiplex, STARsolo and zUMI) that have been developed to process SPLiT-seq data. We provide an overview of the tools, their computational performance, functionality and impact on downstream processing of the single-cell data, which vary greatly depending on the tool used.

RESULTS

We show that STARsolo, splitpipe and alevin-fry splitp can all handle large amount of data within reasonable time. In contrast, the other five pipelines are slow when handling large datasets. When using smaller dataset, cell barcode results are similar with the exception of SPLiTseq-demultiplex and splitpipeline. LR-splitpipe that is originally designed for processing long-read sequencing data is the slowest of all pipelines. Alevin-fry produced different down-stream results that are difficult to interpret. STARsolo functions nearly identical to splitpipe and produce results that are highly similar to each other. However, STARsolo lacks the function to collapse random hexamer reads for which some additional coding is required.

CONCLUSION

Our comprehensive comparative analysis aids users in selecting the most suitable analysis tool for efficient SPLiT-seq data processing, while also detailing the specific prerequisites for each of these pipelines. From the available pipelines, we recommend splitpipe or STARSolo for SPLiT-seq data analysis.

Single-cell analysis identified POSTN+ cells associated with the aggressive phenotype and risk of esophageal squamous cell carcinoma

Tumors are intricate and heterogeneous systems characterized by mosaic cancer cell populations with diverse expression profiles. Leveraging single-cell technologies, we employed the Scissor algorithm to delineate an epithelial subpopulation associated with the aggressive phenotype in esophageal squamous cell carcinoma (ESCC). This identified subpopulation exhibited elevated expression of genes involved in critical pathways, such as epithelial-mesenchymal transition and PI3K-Akt. Key signature genes within this subpopulation, namely CAV1, COL3A1, COL6A1, POSTN, and TAGLN, demonstrated significant upregulation concomitant with both tumorigenesis and tumor progression across independent single-cell datasets. Furthermore, we selected 1,450 expression quantitative trait loci of the top 62 signature genes of this cell subpopulation to investigate their potential in predicting ESCC risk. The results showed that the POSTN loci were predominantly associated with ESCC susceptibility. Through functional annotation and replication analyses, we identified that the rs1028728 in the POSTN promoter was significantly associated with increased ESCC risk in 7,049 ESCC cases and 8,063 controls (odds ratio = 1.29, 95% confidence interval: 1.18-1.42, p = 4.03 × 10-8). Subsequent biochemical experiments showed that the rs1028728[T] allele enhanced POSTN expression by affecting the binding of PRRX1 in the POSTN promoter. In summary, our meticulous single-cell analysis delineates an invasive epithelial subpopulation in ESCC, with POSTN emerging as an important marker for the aggressive phenotype. These findings offer more insights into potential strategies for the prevention and intervention of ESCC, enriching our understanding of this complex cancer landscape.

Advancing predictive markers in lung adenocarcinoma: A machine learning-based immunotherapy prognostic prediction signature

The prognosis of lung adenocarcinoma (LUAD) is generally poor. Immunotherapy has emerged as a promising therapeutic modality, demonstrating remarkable potential for substantially prolonging the overall survival of individuals afflicted with LUAD. However, there is currently a lack of reliable signatures for identifying patients who would benefit from immunotherapy. We conducted a comparative analysis of two immunotherapy cohorts (OAK and POPLAR) and utilized single-factor COX regression to identify genes that significantly impact the prognosis of LUAD. Based on the TCGA-LUAD dataset, we employed a combination of 101 machine learning algorithms to construct a model and selected the optimal model. The model was validated on five GEO datasets and compared with 144 previously published signatures to assess its performance. Subsequently, we explored the underlying biological mechanisms through tumor mutation burden analysis, enrichment analysis, and immune infiltration analysis. An immunotherapy prognostic prediction signature (IPPS) was constructed based on 13 genes, showing robust performance in the TCGA-LUAD dataset. IPPS exhibited consistent predictive accuracy in the validation cohorts. Compared to 144 previously published signatures, IPPS consistently ranked among the top in terms of C-index values. Further exploration revealed differences between high and low-IPPS groups in terms of tumor mutation burden, pathway enrichment, and immune infiltration. IPPS demonstrates strong predictive capabilities for the prognosis of LUAD patients, offering the potential to identify suitable candidates for immunotherapy and contribute to precision treatment strategies for LUAD.

Integrating the analysis of human biopsies using post-translational modifications proteomics

Proteome diversities and their biological functions are significantly amplified by post-translational modifications (PTMs) of proteins. Shotgun proteomics, which does not typically survey PTMs, provides an incomplete picture of the complexity of human biopsies in health and disease. Recent advances in mass spectrometry-based proteomic techniques that enrich and study PTMs are helping to uncover molecular detail from the cellular level to system-wide functions, including how the microbiome impacts human diseases. Protein heterogeneity and disease complexity are challenging factors that make it difficult to characterize and treat disease. The search for clinical biomarkers to characterize disease mechanisms and complexity related to patient diagnoses and treatment has proven challenging. Knowledge of PTMs is fundamentally lacking. Characterization of complex human samples that clarify the role of PTMs and the microbiome in human diseases will result in new discoveries. This review highlights the key role of proteomic techniques used to characterize unknown biological functions of PTMs derived from complex human biopsies. Through the integration of diverse methods used to profile PTMs, this review explores the genetic regulation of proteoforms, cells of origin expressing specific proteins, and several bioactive PTMs and their subsequent analyses by liquid chromatography and tandem mass spectrometry.

Tumor-associated macrophage subtypes on cancer immunity along with prognostic analysis and SPP1-mediated interactions between tumor cells and macrophages

Tumor-associated macrophages (TAM) subtypes have been shown to impact cancer prognosis and resistance to immunotherapy. However, there is still a lack of systematic investigation into their molecular characteristics and clinical relevance in different cancer types. Single-cell RNA sequencing data from three different tumor types were used to cluster and type macrophages. Functional analysis and communication of TAM subpopulations were performed by Gene Ontology-Biological Process and CellChat respectively. Differential expression of characteristic genes in subpopulations was calculated using zscore as well as edgeR and Wilcoxon rank sum tests, and subsequently gene enrichment analysis of characteristic genes and anti-PD-1 resistance was performed by the REACTOME database. We revealed the heterogeneity of TAM, and identified eleven subtypes and their impact on prognosis. These subtypes expressed different molecular functions respectively, such as being involved in T cell activation, apoptosis and differentiation, or regulating viral bioprocesses or responses to viruses. The SPP1 pathway was identified as a critical mediator of communication between TAM subpopulations, as well as between TAM and epithelial cells. Macrophages with high expression of SPP1 resulted in poorer survival. By in vitro study, we showed SPP1 mediated the interactions between TAM clusters and between TAM and tumor cells. SPP1 promoted the tumor-promoting ability of TAM, and increased PDL1 expression and stemness of tumor cells. Inhibition of SPP1 attenuated N-cadherin and β-catenin expression and the activation of AKT and STAT3 pathway in tumor cells. Additionally, we found that several subpopulations could decrease the sensitivity of anti-PD-1 therapy in melanoma. SPP1 signal was a critical pathway of communication between macrophage subtypes. Some specific macrophage subtypes were associated with immunotherapy resistance and prognosis in some cancer types.

The Extracellular Niche and Tumor Microenvironment Enhance KRAS Inhibitor Efficacy in Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease that lacks effective treatment options, highlighting the need for developing new therapeutic interventions. Here, we assessed the response to pharmacologic inhibition of KRAS, the central oncogenic driver of PDAC. In a panel of PDAC cell lines, inhibition of KRASG12D with MRTX1133 yielded variable efficacy in suppressing cell growth and downstream gene expression programs in 2D cultures. On the basis of CRISPR-Cas9 loss-of-function screens, ITGB1 was identified as a target to enhance the therapeutic response to MRTX1133 by regulating mechanotransduction signaling and YAP/TAZ expression, which was confirmed by gene-specific knockdown and combinatorial drug synergy. Interestingly, MRTX1133 was considerably more efficacious in 3D cell cultures. Moreover, MRTX1133 elicited a pronounced cytostatic effect in vivo and controlled tumor growth in PDAC patient-derived xenografts. In syngeneic models, KRASG12D inhibition led to tumor regression that did not occur in immune-deficient hosts. Digital spatial profiling on tumor tissues indicated that MRTX1133-mediated KRAS inhibition enhanced IFNγ signaling and induced antigen presentation that modulated the tumor microenvironment. Further investigation of the immunologic response using single-cell sequencing and multispectral imaging revealed that tumor regression was associated with suppression of neutrophils and influx of effector CD8+ T cells. Together, these findings demonstrate that both tumor cell-intrinsic and -extrinsic events contribute to response to MRTX1133 and credential KRASG12D inhibition as a promising therapeutic strategy for a large percentage of patients with PDAC.

SIGNIFICANCE

Pharmacologic inhibition of KRAS elicits varied responses in pancreatic cancer 2D cell lines, 3D organoid cultures, and xenografts, underscoring the importance of mechanotransduction and the tumor microenvironment in regulating therapeutic responses.

Single-cell RNA sequencing reveals the immune features and viral tropism in the central nervous system of mice infected with Japanese encephalitis virus

Japanese encephalitis virus (JEV) is a neurotropic pathogen that causes lethal encephalitis. The high susceptibility and massive proliferation of JEV in neurons lead to extensive neuronal damage and inflammation within the central nervous system. Despite extensive research on JEV pathogenesis, the effect of JEV on the cellular composition and viral tropism towards distinct neuronal subtypes in the brain is still not well comprehended. To address these issues, we performed single-cell RNA sequencing (scRNA-seq) on cells isolated from the JEV-highly infected regions of mouse brain. We obtained 88,000 single cells and identified 34 clusters representing 10 major cell types. The scRNA-seq results revealed an increasing amount of activated microglia cells and infiltrating immune cells, including monocytes & macrophages, T cells, and natural killer cells, which were associated with the severity of symptoms. Additionally, we observed enhanced communication between individual cells and significant ligand-receptor pairs related to tight junctions, chemokines and antigen-presenting molecules upon JEV infection, suggesting an upregulation of endothelial permeability, inflammation and antiviral response. Moreover, we identified that Baiap2-positive neurons were highly susceptible to JEV. Our findings provide valuable clues for understanding the mechanism of JEV induced neuro-damage and inflammation as well as developing therapies for Japanese encephalitis.

Advancements in technology for characterizing the tumor immune microenvironment

Immunotherapy plays a key role in cancer treatment, however, responses are limited to a small number of patients. The biological basis for the success of immunotherapy is the complex interaction between tumor cells and tumor immune microenvironment (TIME). Historically, research on tumor immune constitution was limited to the analysis of one or two markers, more novel technologies are needed to interpret the complex interactions between tumor cells and TIME. In recent years, major advances have already been made in depicting TIME at a considerably elevated degree of throughput, dimensionality and resolution, allowing dozens of markers to be labeled simultaneously, and analyzing the heterogeneity of tumour-immune infiltrates in detail at the single cell level, depicting the spatial landscape of the entire microenvironment, as well as applying artificial intelligence (AI) to interpret a large amount of complex data from TIME. In this review, we summarized emerging technologies that have made contributions to the field of TIME, and provided prospects for future research.

Single-cell transcriptional signature-based drug repurposing and in vitro evaluation in colorectal cancer

BACKGROUND

The need for intelligent and effective treatment of diseases and the increase in drug design costs have raised drug repurposing as one of the effective strategies in biomedicine. There are various computational methods for drug repurposing, one of which is using transcription signatures, especially single-cell RNA sequencing (scRNA-seq) data, which show us a clear and comprehensive view of the inside of the cell to compare the state of disease and health.

METHODS

In this study, we used 91,103 scRNA-seq samples from 29 patients with colorectal cancer (GSE144735 and GSE132465). First, differential gene expression (DGE) analysis was done using the ASAP website. Then we reached a list of drugs that can reverse the gene signature pattern from cancer to normal using the iLINCS website. Further, by searching various databases and articles, we found 12 drugs that have FDA approval, and so far, no one has reported them as a drug in the treatment of any cancer. Then, to evaluate the cytotoxicity and performance of these drugs, the MTT assay and real-time PCR were performed on two colorectal cancer cell lines (HT29 and HCT116).

RESULTS

According to our approach, 12 drugs were suggested for the treatment of colorectal cancer. Four drugs were selected for biological evaluation. The results of the cytotoxicity analysis of these drugs are as follows: tezacaftor (IC10 = 19 µM for HCT-116 and IC10 = 2 µM for HT-29), fenticonazole (IC10 = 17 µM for HCT-116 and IC10 = 7 µM for HT-29), bempedoic acid (IC10 = 78 µM for HCT-116 and IC10 = 65 µM for HT-29), and famciclovir (IC10 = 422 µM for HCT-116 and IC10 = 959 µM for HT-29).

CONCLUSIONS

Cost, time, and effectiveness are the main challenges in finding new drugs for diseases. Computational approaches such as transcriptional signature-based drug repurposing methods open new horizons to solve these challenges. In this study, tezacaftor, fenticonazole, and bempedoic acid can be introduced as promising drug candidates for the treatment of colorectal cancer. These drugs were evaluated in silico and in vitro, but it is necessary to evaluate them in vivo.

Recent advances in differential expression analysis for single-cell RNA-seq and spatially resolved transcriptomic studies

Differential expression (DE) analysis is a necessary step in the analysis of single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) data. Unlike traditional bulk RNA-seq, DE analysis for scRNA-seq or SRT data has unique characteristics that may contribute to the difficulty of detecting DE genes. However, the plethora of DE tools that work with various assumptions makes it difficult to choose an appropriate one. Furthermore, a comprehensive review on detecting DE genes for scRNA-seq data or SRT data from multi-condition, multi-sample experimental designs is lacking. To bridge such a gap, here, we first focus on the challenges of DE detection, then highlight potential opportunities that facilitate further progress in scRNA-seq or SRT analysis, and finally provide insights and guidance in selecting appropriate DE tools or developing new computational DE methods.

Caveolin-1 gene expression provides additional prognostic information combined with PAM50 risk of recurrence (ROR) score in breast cancer

Combining information from the tumor microenvironment (TME) with PAM50 Risk of Recurrence (ROR) score could improve breast cancer prognostication. Caveolin-1 (CAV1) is a marker of an active TME. CAV1 is a membrane protein involved in cell signaling, extracellular matrix organization, and tumor-stroma interactions. We sought to investigate CAV1 gene expression in relation to PAM50 subtypes, ROR score, and their joint prognostic impact. CAV1 expression was compared between PAM50 subtypes and ROR categories in two cohorts (SCAN-B, n = 5326 and METABRIC, n = 1980). CAV1 expression was assessed in relation to clinical outcomes using Cox regression and adjusted for clinicopathological predictors. Effect modifications between CAV1 expression and ROR categories on clinical outcome were investigated using multiplicative and additive two-way interaction analyses. Differential gene expression and gene set enrichment analyses were applied to compare high and low expressing CAV1 tumors. All samples expressed CAV1 with the highest expression in the Normal-like subtype. Gene modules consistent with epithelial-mesenchymal transition (EMT), hypoxia, and stromal activation were associated with high CAV1 expression. CAV1 expression was inversely associated with ROR category. Interactions between CAV1 expression and ROR categories were observed in both cohorts. High expressing CAV1 tumors conferred worse prognosis only within the group classified as ROR high. ROR gave markedly different prognostic information depending on the underlying CAV1 expression. CAV1, a potential mediator between the malignant cells and TME, could be a useful biomarker that enhances and further refines PAM50 ROR risk stratification in patients with ROR high tumors and a potential therapeutic target.

scRNA-seq analysis discovered suppression of immunomodulatory dependent inflammatory response in PMBCs exposed to silver nanoparticles

The assessment of AgNPs toxicity in vitro and in vivo models are frequently conflicting and inaccurate. Nevertheless, single cell immunological responses in a heterogenous environment have received little attention. Therefore, in this study, we have performed in-depth analysis which clearly revealed cellular-metal ion association as well as specific immunological response. Our study didn't show significant population differences in PMBC between control and AgNPs group implying no toxicological response. To confirm it further, deep profiling identified differences in subsets and differentially expressed genes (DEGs) of monocytes, B cells and T cells. Notably, monocyte subsets showed significant upregulation of metallothionein (MT) gene expression such as MT1G, MT1X, MT1E, MT1A, and MT1F. On the other hand, downregulation of pro-inflammatory genes such as IL1β and CCL3 in both CD16 + and CD16- monocyte subsets were observed. This result indicated that AgNPs association with monocyte subsets de-promoted inflammatory responsive genes suggesting no significant toxicity observed in AgNPs treated group. Other cell types such as B cells and T cells also showed negligible differences in their subsets suggesting no toxicity response. Further, AgNPs treated group showed upregulation of cell proliferation, ribosomal synthesis, downregulation of cytokine release, and T cell differentiation inhibition. Overall, our results conclude that treatment of AgNPs to PMBC cells didn't display immunological related cytotoxicity response and thus motivate researchers to use them actively for biomedical applications.

Unique Spatial Transcriptomic Profiling of the Murine Femoral Fracture Callus: A Preliminary Report

Fracture callus formation is a dynamic stage of bone activity and repair with precise, spatially localized gene expression. Metastatic breast cancer impairs fracture healing by disrupting bone homeostasis and imparting an altered genomic profile. Previous sequencing techniques such as single-cell RNA and in situ hybridization are limited by missing spatial context and low throughput, respectively. We present a preliminary approach using the Visium CytAssist spatial transcriptomics platform to provide the first spatially intact characterization of genetic expression changes within an orthopedic model of impaired fracture healing. Tissue slides prepared from BALB/c mice with or without MDA-MB-231 metastatic breast cancer cells were used. Both unsupervised clustering and histology-based annotations were performed to identify the hard callus, soft callus, and interzone for differential gene expression between the wild-type and pathological fracture model. The spatial transcriptomics platform successfully localized validated genes of the hard (Dmp1, Sost) and soft callus (Acan, Col2a1). The fibrous interzone was identified as a region of extensive genomic heterogeneity. MDA-MB-231 samples demonstrated downregulation of the critical bone matrix and structural regulators that may explain the weakened bone structure of pathological fractures. Spatial transcriptomics may represent a valuable tool in orthopedic research by providing temporal and spatial context.

Cell fate decision in erythropoiesis: Insights from multiomics studies

Every second, the body produces 2 million red blood cells through a process called erythropoiesis. Erythropoiesis is hierarchical in that it results from a series of cell fate decisions whereby hematopoietic stem cells progress toward the erythroid lineage. Single-cell transcriptomic and proteomic approaches have revolutionized the way we understand erythropoiesis, revealing it to be a gradual process that underlies a progressive restriction of fate potential driven by quantitative changes in lineage-specifying transcription factors. Despite these major advances, we still know very little about what cell fate decision entails at the molecular level. Novel approaches that simultaneously measure additional properties in single cells, including chromatin accessibility, transcription factor binding, and/or cell surface proteins are being developed at a fast pace, providing the means to exciting new advances in the near future. In this review, we briefly summarize the main findings obtained from single-cell studies of erythropoiesis, highlight outstanding questions, and suggest recent technological advances to address them.