Fragmentation of Small-Cell Lung Cancer Regulatory States in Heterotypic Microenvironments

Laser capture microdissection for biomedical research: towards high-throughput, multi-omics, and single-cell resolution

Spatial omics technologies have become powerful methods to provide valuable insights into cells and tissues within a complex context, significantly enhancing our understanding of the intricate and multifaceted biological system. With an increasing focus on spatial heterogeneity, there is a growing need for unbiased, spatially resolved omics technologies. Laser capture microdissection (LCM) is a cutting-edge method for acquiring spatial information that can quickly collect regions of interest (ROIs) from heterogeneous tissues, with resolutions ranging from single cells to cell populations. Thus, LCM has been widely used for studying the cellular and molecular mechanisms of diseases. This review focuses on the differences among four types of commonly used LCM technologies and their applications in omics and disease research. Key attributes of application cases are also highlighted, such as throughput and spatial resolution. In addition, we comprehensively discuss the existing challenges and the great potential of LCM in biomedical research, disease diagnosis, and targeted therapy from the perspective of high-throughput, multi-omics, and single-cell resolution.

Systems approaches to uncovering the contribution of environment-mediated drug resistance

Cancer drug response is heavily influenced by the extracellular matrix (ECM) environment. Despite a clear appreciation that the ECM influences cancer drug response and progression, a unified view of how, where, and when environment-mediated drug resistance contributes to cancer progression has not coalesced. Here, we survey some specific ways in which the ECM contributes to cancer resistance with a focus on how materials development can coincide with systems biology approaches to better understand and perturb this contribution. We argue that part of the reason that environment-mediated resistance remains a perplexing problem is our lack of a wholistic view of the entire range of environments and their impacts on cell behavior. We cover a series of recent experimental and computational tools that will aid exploration of ECM reactions space, and how they might be synergistically integrated.

High-throughput single-сell sequencing in cancer research

With advances in sequencing and instrument technology, bioinformatics analysis is being applied to batches of massive cells at single-cell resolution. High-throughput single-cell sequencing can be utilized for multi-omics characterization of tumor cells, stromal cells or infiltrated immune cells to evaluate tumor progression, responses to environmental perturbations, heterogeneous composition of the tumor microenvironment, and complex intercellular interactions between these factors. Particularly, single-cell sequencing of T cell receptors, alone or in combination with single-cell RNA sequencing, is useful in the fields of tumor immunology and immunotherapy. Clinical insights obtained from single-cell analysis are critically important for exploring the biomarkers of disease progression or antitumor treatment, as well as for guiding precise clinical decision-making for patients with malignant tumors. In this review, we summarize the clinical applications of single-cell sequencing in the fields of tumor cell evolution, tumor immunology, and tumor immunotherapy. Additionally, we analyze the tumor cell response to antitumor treatment, heterogeneity of the tumor microenvironment, and response or resistance to immune checkpoint immunotherapy. The limitations of single-cell analysis in cancer research are also discussed.

Natural products: Potential targets of TME related long non-coding RNAs in lung cancer

BACKGROUND

Lung cancer is a significant health concern worldwide due to high mortality and morbidity, despite the advances in diagnosis, treatment, and management. Recent experimental evidence from different models suggested long non-coding RNAs (lncRNAs) as major modulators of cancer stem cells (CSCs) in the tumor microenvironment (TME) to support metastasis and drug resistance in lung cancer. Evidence-based studies demonstrated that natural products interfere with TME functions.

PURPOSE OF STUDY

To establish lncRNAs of TME as novel targets of natural compounds for lung cancer management.

STUDY DESIGN

Current study used a combination of TME and lung CSCs, lncRNAs and enrichment and stemness maintenance, natural products and stem cell management, natural products and lncRNAs, natural products and targeted delivery as keywords to retrieve the literature from Scopus, Web of Science, PubMed, and Google Scholar. This study critically reviewed the current literature and presented cancer stem cells' ability in reprogramming lung TME.

RESULTS

This review found that TME related oncogenic and tumor suppressor lncRNAs and their signaling pathways control the maintenance of stemness in lung TME. This review explored natural phenolic compounds and found that curcumin, genistein, quercetin epigallocatechin gallate and ginsenoside Rh2 are efficient in managing lung CSCs. They modulate lncRNAs and their upstream mediators by targeting signaling and epigenetic pathways. This review also identified relevant nanotechnology-based phytochemical delivery approaches for targeting lung cancer.

CONCLUSION

By critical literature analysis, TME related lncRNAs were identified as potential therapeutic targets, aiming to develop natural product-based therapeutics to treat metastatic and drug-resistant lung cancers.

Laser Capture Proteomics: spatial tissue molecular profiling from the bench to personalized medicine

INTRODUCTION

Laser Capture Microdissection (LCM) uses a laser to isolate, or capture, specific cells of interest in a complex heterogeneous tissue section, under direct microscopic visualization. Recently, there has been a surge of publications using LCM for tissue spatial molecular profiling relevant to a wide range of research topics.

AREAS COVERED

We summarize the many advances in tissue Laser Capture Proteomics (LCP) using mass spectrometry for discovery, and protein arrays for signal pathway network mapping. This review emphasizes: a) transition of LCM phosphoproteomics from the lab to the clinic for individualized cancer therapy, and b) the emerging frontier of LCM single cell molecular analysis combining proteomics with genomic, and transcriptomic analysis. The search strategy was based on the combination of MeSH terms with expert refinement.

EXPERT OPINION

LCM is complemented by a rich set of instruments, methodology protocols, and analytical A.I. (artificial intelligence) software for basic and translational research. Resolution is advancing to the tissue single cell level. A vision for the future evolution of LCM is presented. Emerging LCM technology is combining digital and AI guided remote imaging with automation, and telepathology, to a achieve multi-omic profiling that was not previously possible.

Pan-Cancer Drivers Are Recurrent Transcriptional Regulatory Heterogeneities in Early-Stage Luminal Breast Cancer

The heterogeneous composition of solid tumors is known to impact disease progression and response to therapy. Malignant cells coexist in different regulatory states that can be accessed transcriptomically by single-cell RNA sequencing, but these methods have many caveats related to sensitivity, noise, and sample handling. We revised a statistical fluctuation analysis called stochastic profiling to combine with 10-cell RNA sequencing, which was designed for laser-capture microdissection (LCM) and extended here for immuno-LCM. When applied to a cohort of late-onset, early-stage luminal breast cancers, the integrated approach identified thousands of candidate regulatory heterogeneities. Intersecting the candidates from different tumors yielded a relatively stable set of 710 recurrent heterogeneously expressed genes (RHEG), which were significantly variable in >50% of patients. RHEGs were not strongly confounded by dissociation artifacts, cell-cycle oscillations, or driving mutations for breast cancer. Rather, RHEGs were enriched for epithelial-to-mesenchymal transition genes and, unexpectedly, the latest pan-cancer assembly of driver genes across cancer types other than breast. These findings indicate that heterogeneous transcriptional regulation conceivably provides a faster, reversible mechanism for malignant cells to evaluate the effects of potential oncogenes or tumor suppressors on cancer hallmarks. SIGNIFICANCE: Profiling intratumor heterogeneity of luminal breast carcinoma cells identifies a recurrent set of genes, suggesting sporadic activation of pathways known to drive other types of cancer.See related articles by Schaff and colleagues, p. 1853 and Sutcliffe and colleagues, p. 1868.

Premalignant Oligodendrocyte Precursor Cells Stall in a Heterogeneous State of Replication Stress Prior to Gliomagenesis

Cancer evolves from premalignant clones that adopt unusual cell states to achieve transformation. We previously pinpointed the oligodendrocyte precursor cell (OPC) as a cell of origin for glioma, but the early changes of mutant OPCs during premalignancy remained unknown. Using mice engineered for inducible Nf1-Trp53 loss in OPCs, we acutely isolated labeled mutant OPCs by laser-capture microdissection, determined global gene-expression changes by bulk RNA sequencing, and compared with cell-state fluctuations at the single-cell level by stochastic profiling, which uses RNA-sequencing measurements from random pools of 10 mutant cells. At 12 days after Nf1-Trp53 deletion, bulk differences were mostly limited to mitotic hallmarks and genes for ribosome biosynthesis, and stochastic profiling revealed a spectrum of stem-progenitor (Axl, Aldh1a1), proneural, and mesenchymal states as potential starting points for gliomagenesis. At 90 days, bulk sequencing detected few differentially expressed transcripts, whereas stochastic profiling revealed cell states for neurons and mural cells that do not give rise to glial tumors, suggesting cellular dead-ends for gliomagenesis. Importantly, mutant OPCs that strongly expressed key effectors of nonsense-mediated decay (Upf3b) and homology-dependent DNA repair (Rad51c, Slx1b, Ercc4) were identified along with DNA-damage markers, suggesting transcription-associated replication stress. Analysis of 10-cell transcriptomes at 90 days identified a locus of elevated gene expression containing an additional repair endonuclease (Mus81) and Rin1, a Ras-Raf antagonist and possible counterbalance to Nf1 loss, which was microdeleted or downregulated in gliomas at 150 days. These hidden cell-state variations uncover replication stress as a potential bottleneck that must be resolved for glioma initiation. SIGNIFICANCE: Profiling premalignant cell states in a mouse model of glioma uncovers regulatory heterogeneity in glioma cells-of-origin and defines a state of replication stress that precedes tumor initiation.See related articles by Singh and colleagues, p. 1840 and Schaff and colleagues, p. 1853.

Modeling the complete kinetics of coxsackievirus B3 reveals human determinants of host-cell feedback

Complete kinetic models are pervasive in chemistry but lacking in biological systems. We encoded the complete kinetics of infection for coxsackievirus B3 (CVB3), a compact and fast-acting RNA virus. The model consists of separable, detailed modules describing viral binding-delivery, translation-replication, and encapsidation. Specific module activities are dampened by the type I interferon response to viral double-stranded RNAs (dsRNAs), which is itself disrupted by viral proteinases. The experimentally validated kinetics uncovered that cleavability of the dsRNA transducer mitochondrial antiviral signaling protein (MAVS) becomes a stronger determinant of viral outcomes when cells receive supplemental interferon after infection. Cleavability is naturally altered in humans by a common MAVS polymorphism, which removes a proteinase-targeted site but paradoxically elevates CVB3 infectivity. These observations are reconciled with a simple nonlinear model of MAVS regulation. Modeling complete kinetics is an attainable goal for small, rapidly infecting viruses and perhaps viral pathogens more broadly. A record of this paper's transparent peer review process is included in the Supplemental information.