Single-cell immunoblotting resolves estrogen receptor-α isoforms in breast cancer

Deciphering cellular complexity: advances and future directions in single-cell protein analysis

Single-cell protein analysis has emerged as a powerful tool for understanding cellular heterogeneity and deciphering the complex mechanisms governing cellular function and fate. This review provides a comprehensive examination of the latest methodologies, including sophisticated cell isolation techniques (Fluorescence-Activated Cell Sorting (FACS), Magnetic-Activated Cell Sorting (MACS), Laser Capture Microdissection (LCM), manual cell picking, and microfluidics) and advanced approaches for protein profiling and protein-protein interaction analysis. The unique strengths, limitations, and opportunities of each method are discussed, along with their contributions to unraveling gene regulatory networks, cellular states, and disease mechanisms. The importance of data analysis and computational methods in extracting meaningful biological insights from the complex data generated by these technologies is also highlighted. By discussing recent progress, technological innovations, and potential future directions, this review emphasizes the critical role of single-cell protein analysis in advancing life science research and its promising applications in precision medicine, biomarker discovery, and targeted therapeutics. Deciphering cellular complexity at the single-cell level holds immense potential for transforming our understanding of biological processes and ultimately improving human health.

Identification of the novel exhausted T cell CD8 + markers in breast cancer

Cancer is one of the most concerning public health issues and breast cancer is one of the most common cancers in the world. The immune cells within the tumor microenvironment regulate cancer development. In this study, single immune cell data sets were used to identify marker gene sets for exhausted CD8 + T cells (CD8Tex) in breast cancer. Machine learning methods were used to cluster subtypes and establish the prognostic models with breast cancer bulk data using the gene sets to evaluate the impacts of CD8Tex. We analyzed breast cancer overexpressing and survival-associated marker genes and identified CD8Tex hub genes in the protein-protein-interaction network. The relevance of the hub genes for CD8 + T-cells in breast cancer was evaluated. The clinical associations of the hub genes were analyzed using bulk sequencing data and spatial sequencing data. The pan-cancer expression, survival, and immune association of the hub genes were analyzed. We identified biomarker gene sets for CD8Tex in breast cancer. CD8Tex-based subtyping systems and prognostic models performed well in the separation of patients with different immune relevance and survival. CRTAM, CLEC2D, and KLRB1 were identified as CD8Tex hub genes and were demonstrated to have potential clinical relevance and immune therapy impact. This study provides a unique view of the critical CD8Tex hub genes for cancer immune therapy.

Advancing Breast Cancer Heterogeneity Analysis: Insights from Genomics, Transcriptomics and Proteomics at Bulk and Single-Cell Levels

Breast cancer continues to pose a significant healthcare challenge worldwide for its inherent molecular heterogeneity. This review offers an in-depth assessment of the molecular profiling undertaken to understand this heterogeneity, focusing on multi-omics strategies applied both in traditional bulk and single-cell levels. Genomic investigations have profoundly informed our comprehension of breast cancer, enabling its categorization into six intrinsic molecular subtypes. Beyond genomics, transcriptomics has rendered deeper insights into the gene expression landscape of breast cancer cells. It has also facilitated the formulation of more precise predictive and prognostic models, thereby enriching the field of personalized medicine in breast cancer. The comparison between traditional and single-cell transcriptomics has identified unique gene expression patterns and facilitated the understanding of cell-to-cell variability. Proteomics provides further insights into breast cancer subtypes by illuminating intricate protein expression patterns and their post-translational modifications. The adoption of single-cell proteomics has been instrumental in this regard, revealing the complex dynamics of protein regulation and interaction. Despite these advancements, this review underscores the need for a holistic integration of multiple 'omics' strategies to fully decipher breast cancer heterogeneity. Such integration not only ensures a comprehensive understanding of breast cancer's molecular complexities, but also promotes the development of personalized treatment strategies.

ESR1 activating mutations: From structure to clinical application

Estrogen receptor-positive breast cancer is the most common type of both early and advanced breast cancer. Estrogen receptor alpha (ER) is a nuclear hormone receptor and a key driver of tumorigenesis and tumor progression in these breast cancers. As such, it is a key treatment target and a biomarker predictive of response to endocrine therapy. Activating ESR1 ligand binding domain mutations engender constitutive/ligand independent transcriptional activities and emerge following prolonged first-line hormone therapy regimens, mainly from aromatase inhibitors. The full scale of the biological and clinical significance of these mutations continue to evolve and additional studies are required to further discern the multimodal effects of these mutations on ER transcription, metastatic propensity, and the tumor microenvironment. Furthermore, recent and ongoing studies highlight the potential clinical utility of these mutations as therapeutic targets and dynamic biomarkers. Herein, we review the structure, functional consequences, and clinical implications of the activating ESR1 mutations in advanced estrogen receptor-positive breast cancer.

Interactions Between Natural Products and Tamoxifen in Breast Cancer: A Comprehensive Literature Review

Introduction: Tamoxifen (TAM) is the most commonly used hormone therapeutic drug for the treatment of estrogen receptor-positive (ER+) breast cancer. 30%–70% of clinical breast cancer patients use natural products, which may increase the likelihood of drug interactions.

Objective: To evaluate the evidence for the interactions between natural products and TAM in breast cancer.

Methods: Electronic databases, including PubMed, CINAHL Plus (via EbscoHost), European PMC, Medline, and Google Scholar, were searched for relevant publications. The search terms include complementary and alternative medicine, natural products, plant products, herbs, interactions, tamoxifen, breast cancer, and their combinations.

Results: Various in vitro and in vivo studies demonstrated that the combined use of natural products with TAM produced synergistic anti-cancer effects, including improved inhibition of tumor cell growth and TAM sensitivity and reduced side effects or toxicity of TAM. In contrast, some natural products, including Angelica sinensis (Oliv.) Diels [Apiaceae], Paeonia lactiflora Pall., Rehmannia glutinosa (Gaertn.) DC., Astragalus mongholicus Bunge, and Glycyrrhiza glabra L. [Fabaceae], showed estrogen-like activity, which may reduce the anti-cancer effect of TAM. Some natural products, including morin, silybin, epigallocatechin gallate (EGCG), myricetin, baicalein, curcumin, kaempferol, or quercetin, were found to increase the bioavailability of TAM and its metabolites in vivo. However, three are limited clinical studies on the combination of natural products and TAM.

Conclusion: There is evidence for potential interactions of various natural products with TAM in pre-clinical studies, although the relevant clinical evidence is still lacking. Further studies are warranted to evaluate the potential interactions of natural products with TAM in clinical settings.

The Intriguing Landscape of Single-Cell Protein Analysis

Profiling protein expression at single-cell resolution is essential for fundamental biological research (such as cell differentiation and tumor microenvironmental examination) and clinical precision medicine where only a limited number of primary cells are permitted. With the recent advances in engineering, chemistry, and biology, single-cell protein analysis methods are developed rapidly, which enable high-throughput and multiplexed protein measurements in thousands of individual cells. In combination with single cell RNA sequencing and mass spectrometry, single-cell multi-omics analysis can simultaneously measure multiple modalities including mRNAs, proteins, and metabolites in single cells, and obtain a more comprehensive exploration of cellular signaling processes, such as DNA modifications, chromatin accessibility, protein abundance, and gene perturbation. Here, the recent progress and applications of single-cell protein analysis technologies in the last decade are summarized. Current limitations, challenges, and possible future directions in this field are also discussed.

Segmentation-based analysis of single-cell immunoblots

From genomics to transcriptomics to proteomics, microfluidic tools underpin recent advances in single-cell biology. Detection of specific proteoforms-with single-cell resolution-presents challenges in detection specificity and sensitivity. Miniaturization of protein immunoblots to single-cell resolution mitigates these challenges. For example, in microfluidic western blotting, protein targets are separated by electrophoresis and subsequently detected using fluorescently labeled antibody probes. To quantify the expression level of each protein target, the fluorescent protein bands are fit to Gaussians; yet, this method is difficult to use with noisy, low-abundance, or low-SNR protein bands, and with significant band skew or dispersion. In this study, we investigate segmentation-based approaches to robustly quantify protein bands from single-cell protein immunoblots. As compared to a Gaussian fitting pipeline, the segmentation pipeline detects >1.5× more protein bands for downstream quantification as well as more of the low-abundance protein bands (i.e., with SNR ∼3). Utilizing deep learning-based segmentation approaches increases the recovery of low-SNR protein bands by an additional 50%. However, we find that segmentation-based approaches are less robust at quantifying poorly resolved protein bands (separation resolution, Rs < 0.6). With burgeoning needs for more single-cell protein analysis tools, we see microfluidic separations as benefitting substantially from segmentation-based analysis approaches.